diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 0000000..0fe6a27
--- /dev/null
+++ b/.coveragerc
@@ -0,0 +1,13 @@
+[paths]
+source =
+ src/crane_controller
+ */site-packages/crane_controller
+
+[run]
+source = crane_controller
+branch = True
+
+[report]
+fail_under = 10.0
+show_missing = True
+skip_covered = True
diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 0000000..6dda6af
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,15 @@
+# EditorConfig is awesome: https://EditorConfig.org
+
+# top-most EditorConfig file
+root = true
+
+[*]
+indent_style = space
+indent_size = 4
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+
+[*.{yml,yaml}]
+indent_style = space
+indent_size = 2
diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..164ad77
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,36 @@
+# Set the default behavior, in case people don't have core.autocrlf set.
+* text=auto
+
+# Explicitly declare text files you want to always be normalized and converted
+# to native line endings on checkout.
+*.py text
+*.cpp text
+*.hpp text
+*.c text
+*.h text
+*.json text
+*.xml text
+*.txt text
+*.yml text
+*.yaml text
+*.toml text
+*.rst text
+*.ini text
+
+# Declare files that will always have CRLF line endings on checkout.
+*.vcproj text eol=crlf
+*.sln text eol=crlf
+*.md text eol=crlf
+
+# Declare files that will always have LF line endings on checkout.
+*.sh text eol=lf
+
+# Declare files that will not be normalized regardless of their content.
+*.jpg -text
+*.png -text
+*.gif -text
+*.ico -text
+
+# Denote all files that are truly binary and should not be modified.
+*.onnx binary
+*.fmu binary
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
new file mode 100644
index 0000000..e59418a
--- /dev/null
+++ b/.github/copilot-instructions.md
@@ -0,0 +1,98 @@
+# GitHub Copilot – Project Instructions
+
+This repository uses a **hybrid instruction model**:
+
+- **Portable, tool-agnostic rules live in the repository root**
+- **GitHub-specific scoping and activation live under `.github/`**
+
+GitHub Copilot and GitHub Agents must treat the files below as the
+authoritative source of project behavior.
+
+---
+
+## Primary Instruction Sources (Authoritative)
+
+Always load and follow these files first:
+
+- `/.instructions.md`
+ Project-wide engineering rules, quality standards, and constraints.
+
+- `/.prompt.md`
+ Default working style and agent execution discipline.
+
+- `/plans/plan.md` or `/plans/plan-draft.md` (when present)
+ Explicit plan / orchestration artifact for Plan Mode workflows.
+ The presence of `/plans/plan.md` or `/plans/plan-draft.md` indicates that a plan-first workflow is expected.
+ `/plans/.plan.md` serves as a template and should not be modified directly.
+
+These files form the **knowledge contract** for this repository and
+are reviewed and maintained like production code.
+
+---
+
+## GitHub-Scoped Instructions (Additive)
+
+Additional GitHub-specific guidance MAY apply from:
+
+- `.github/instructions/*.instructions.md`
+ Narrow, scoped rules using `applyTo` (must not contradict root rules).
+
+- `.github/prompts/*.prompt.md`
+ Reusable GitHub workflows (feature implementation, reviews, refactors).
+
+- `.github/agents/*.agent.md`
+ Named agent personas, if explicitly activated.
+
+GitHub-scoped files **refine or specialize** behavior.
+They must never redefine or override the intent of `/.instructions.md`.
+
+---
+
+## Precedence & Conflict Resolution
+
+If multiple instructions apply, resolve conflicts in this order:
+
+1. Scoped `.github/instructions/*.instructions.md` (most specific — may only **narrow or refine** root rules, never override or contradict them)
+2. `/.instructions.md`
+3. `/.prompt.md`
+
+When in doubt:
+- Prefer correctness, testability, and clarity over speed
+- Ask for clarification rather than guessing
+
+---
+
+## Safety & Boundaries
+
+- Work only within this repository
+- Do not assume network or internet access unless explicitly stated
+- Do not introduce hidden side effects, hooks, or background behavior
+- Do not change unrelated code opportunistically
+
+All agent output is subject to human review and normal PR processes.
+
+---
+
+## Agent Operating Mode
+
+Use a **plan-first workflow** by default:
+
+1. Restate the task and assumptions
+2. Produce or update `plan.md` (unless instructed otherwise)
+3. Wait for confirmation if scope is ambiguous
+4. Implement incrementally
+5. Verify with tests
+6. Summarize changes and remaining risks
+
+This discipline is mandatory for non-trivial changes.
+
+---
+
+## Intentional Non-Goals
+
+- This file does **not** contain detailed coding rules
+ (those belong in `/.instructions.md`)
+- This file does **not** encode organization policy or governance logic
+- This file avoids duplication by design
+
+Less is intentional. Stability is a feature.
diff --git a/.github/workflows/_build_and_publish_documentation.yml b/.github/workflows/_build_and_publish_documentation.yml
new file mode 100644
index 0000000..8df022f
--- /dev/null
+++ b/.github/workflows/_build_and_publish_documentation.yml
@@ -0,0 +1,107 @@
+name: Build and publish documentation
+
+on: workflow_call
+
+env:
+ DEFAULT_BRANCH: 'release'
+ #SPHINXOPTS: '-W --keep-going -T'
+ # ^-- If these SPHINXOPTS are enabled, then be strict about the builds and fail on any warnings
+
+jobs:
+ build-and-publish-docs:
+ name: Build and publish documentation
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout active branch
+ uses: actions/checkout@v5
+ with:
+ lfs: true
+ - name: Install uv
+ uses: astral-sh/setup-uv@v7
+ with:
+ enable-cache: true
+ cache-dependency-glob: 'uv.lock'
+ - name: Install the project
+ run: uv sync --frozen
+ - name: Print debugging information
+ run: |
+ echo "github.ref:" ${{github.ref}}
+ echo "github.event_name:" ${{github.event_name}}
+ echo "github.head_ref:" ${{github.head_ref}}
+ echo "github.base_ref:" ${{github.base_ref}}
+ set -x
+ git rev-parse --abbrev-ref HEAD
+ git branch
+ git branch -a
+ git remote -v
+ uv run python -V
+ uv pip list
+
+ # Build documentation
+ - uses: sphinx-doc/github-problem-matcher@master
+ - name: Build documentation
+ run: |
+ cd docs
+ uv run make html
+
+ - name: Clone and cleanup gh-pages branch
+ run: |
+ set -x
+ git fetch
+ ( git branch gh-pages remotes/origin/gh-pages && git clone . --branch=gh-pages _gh-pages/ ) || mkdir _gh-pages
+ rm -rf _gh-pages/.git/
+ mkdir -p _gh-pages/branch/
+
+ # Delete orphaned branch-folders:
+ # Go through each subfolder in _gh-pages/branch/
+ # If it relates to an orphaned branch, delete it.
+ - name: Delete orphaned branch-folders
+ run: |
+ set -x
+ for brdir in `ls _gh-pages/branch/` ; do
+ brname=${brdir//--/\/} # replace '--' with '/'
+ if ! git show-ref remotes/origin/$brname ; then
+ echo "Removing $brdir"
+ rm -r _gh-pages/branch/$brdir/
+ fi
+ done
+
+ # Copy documentation to _gh-pages/ (if push happened on release branch)
+ - name: Copy documentation to _gh-pages/
+ if: |
+ contains(github.ref, env.DEFAULT_BRANCH)
+ run: |
+ set -x
+ # Delete everything under _gh-pages/ that is from the
+ # primary branch deployment. Excludes the other branches
+ # _gh-pages/branch-* paths, and not including
+ # _gh-pages itself.
+ find _gh-pages/ -mindepth 1 ! -path '_gh-pages/branch*' -delete
+ rsync -a docs/build/html/ _gh-pages/
+
+ # Copy documentation to _gh-pages/branch/$brname (if push happened on any other branch)
+ # ('/' gets replaced by '--')
+ - name: Copy documentation to _gh-pages/branch/${{github.ref}}
+ if: |
+ !contains(github.ref, env.DEFAULT_BRANCH)
+ run: |
+ set -x
+ #brname=$(git rev-parse --abbrev-ref HEAD)
+ brname="${{github.ref}}"
+ brname="${brname##refs/heads/}"
+ brdir=${brname//\//--} # replace '/' with '--'
+ rm -rf _gh-pages/branch/${brdir}
+ rsync -a docs/build/html/ _gh-pages/branch/${brdir}
+
+ # Add .nojekyll file
+ - name: Add .nojekyll file
+ run: touch _gh-pages/.nojekyll
+
+ # Publish: Commit gh-pages branch and publish it to GitHub Pages
+ - name: Publish documentation
+ uses: peaceiris/actions-gh-pages@v4
+ with:
+ publish_branch: gh-pages
+ github_token: ${{ secrets.GITHUB_TOKEN }}
+ publish_dir: _gh-pages/
+ force_orphan: true
diff --git a/.github/workflows/_build_package.yml b/.github/workflows/_build_package.yml
new file mode 100644
index 0000000..509ec20
--- /dev/null
+++ b/.github/workflows/_build_package.yml
@@ -0,0 +1,29 @@
+name: Build Package
+
+on: workflow_call
+
+jobs:
+ build:
+ name: Build source distribution
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v5
+ with:
+ lfs: true
+ - name: Install uv
+ uses: astral-sh/setup-uv@v7
+ with:
+ enable-cache: true
+ cache-dependency-glob: 'uv.lock'
+ - name: Build source distribution and wheels
+ run: uv build
+ - name: Check build artifacts
+ run: uvx twine check --strict dist/*
+ - name: Upload build artifacts
+ uses: actions/upload-artifact@v5
+ with:
+ name: dist
+ path: |
+ dist/*.tar.gz
+ dist/*.whl
diff --git a/.github/workflows/_code_quality.yml b/.github/workflows/_code_quality.yml
new file mode 100644
index 0000000..6d7f696
--- /dev/null
+++ b/.github/workflows/_code_quality.yml
@@ -0,0 +1,68 @@
+name: Code Quality
+
+on: workflow_call
+
+jobs:
+ ruff_format:
+ runs-on: ubuntu-latest
+ name: ruff format
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v5
+ - name: Install uv
+ uses: astral-sh/setup-uv@v7
+ with:
+ enable-cache: true
+ cache-dependency-glob: 'uv.lock'
+ - name: Install the project
+ run: uv sync -U
+ - name: Run ruff format
+ run: uv run ruff format --diff
+
+ ruff_check:
+ runs-on: ubuntu-latest
+ name: ruff check
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v5
+ - name: Install uv
+ uses: astral-sh/setup-uv@v7
+ with:
+ enable-cache: true
+ cache-dependency-glob: 'uv.lock'
+ - name: Install the project
+ run: uv sync -U
+ - name: Run ruff check
+ run: uv run ruff check --diff
+
+ pyright:
+ runs-on: ubuntu-latest
+ name: pyright
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v5
+ - name: Install uv
+ uses: astral-sh/setup-uv@v7
+ with:
+ enable-cache: true
+ cache-dependency-glob: 'uv.lock'
+ - name: Install the project
+ run: uv sync -U
+ - name: Run pyright
+ run: uv run pyright
+
+ mypy:
+ runs-on: ubuntu-latest
+ name: mypy
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v5
+ - name: Install uv
+ uses: astral-sh/setup-uv@v7
+ with:
+ enable-cache: true
+ cache-dependency-glob: 'uv.lock'
+ - name: Install the project
+ run: uv sync -U
+ - name: Run mypy
+ run: uv run mypy
diff --git a/.github/workflows/_merge_into_release.yml b/.github/workflows/_merge_into_release.yml
new file mode 100644
index 0000000..67348de
--- /dev/null
+++ b/.github/workflows/_merge_into_release.yml
@@ -0,0 +1,26 @@
+name: Merge into release
+
+on:
+ workflow_call:
+ secrets:
+ RELEASE_TOKEN:
+ required: true
+
+jobs:
+ merge_into_release:
+ name: Merge ${{ github.event.ref }} -> release branch
+ runs-on: ubuntu-latest
+ environment: release
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v5
+ with:
+ # Fetch the whole history to prevent unrelated history errors
+ fetch-depth: 0
+ # The branch you want to checkout (usually equal to `branchtomerge`)
+ # ref: ${{ github.event.ref }}
+ - uses: devmasx/merge-branch@v1.4.0
+ with:
+ type: now
+ target_branch: release
+ github_token: ${{ secrets.RELEASE_TOKEN }}
diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml
new file mode 100644
index 0000000..b0b1a73
--- /dev/null
+++ b/.github/workflows/_test.yml
@@ -0,0 +1,26 @@
+name: Unit Tests
+
+on: workflow_call
+
+jobs:
+ test:
+ name: Test (Python ${{ matrix.python }}, ${{ matrix.os }})
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: true
+ matrix:
+ os: [ ubuntu-latest, windows-latest ]
+ python: [ '3.11', '3.12', '3.13' ]
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v5
+ - name: Install uv
+ uses: astral-sh/setup-uv@v7
+ with:
+ enable-cache: true
+ cache-dependency-glob: 'uv.lock'
+ - name: Install the project
+ run: uv sync -p ${{ matrix.python }} -U --no-dev
+ - name: Run pytest
+ run: >
+ uv run --with pytest --with pytest-cov pytest --cov -m "not slow"
diff --git a/.github/workflows/_test_future.yml b/.github/workflows/_test_future.yml
new file mode 100644
index 0000000..c3e79f5
--- /dev/null
+++ b/.github/workflows/_test_future.yml
@@ -0,0 +1,41 @@
+name: Unit Tests (py314)
+# Test also with Python 3.14 (experimental; workflow will not fail on error.)
+
+on: workflow_call
+
+jobs:
+ test314:
+ name: Test (Python ${{ matrix.python.version }}, ${{ matrix.os }}) (experimental)
+ continue-on-error: true
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: true
+ matrix:
+ os: [ ubuntu-latest, windows-latest ]
+ python:
+ - version: '3.14.0-alpha - 3.14.0'
+ uvpy: '3.14'
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v5
+ - name: Install uv
+ uses: astral-sh/setup-uv@v7
+ with:
+ enable-cache: true
+ cache-dependency-glob: 'uv.lock'
+ - name: Remove Python upper version constraint from pyproject.toml
+ shell: pwsh
+ run: |
+ $File = Get-Item pyproject.toml
+ $Content = Get-Content $File -Raw
+ [regex]$RequiresPythonPattern = '(?=]{1,2}\s?\d+\.\d+\.?\d*\.?[\d\w]*\s?)(,\s?[<=]{1,2}\s?\d+\.?\d*\.?\d*\.?[\d\w]*\s?)?(")'
+ if ($Content -match $RequiresPythonPattern) {
+ $RequiresPythonFound = $Matches[0]
+ $RequiresPythonReplacement = $Matches[1] + $Matches[3]
+ $Content -Replace $RequiresPythonFound, $RequiresPythonReplacement | Set-Content $File -NoNewline
+ }
+ - name: Install the project
+ run: uv sync -p ${{ matrix.python.uvpy }} -U --no-dev
+ - name: Run pytest
+ run: >
+ uv run --with pytest --with pytest-cov pytest --cov -m "not slow"
diff --git a/.github/workflows/nightly_build.yml b/.github/workflows/nightly_build.yml
new file mode 100644
index 0000000..41eb2a2
--- /dev/null
+++ b/.github/workflows/nightly_build.yml
@@ -0,0 +1,21 @@
+name: Nightly Build
+run-name: Nightly Build (by @${{ github.actor }})
+
+on:
+ schedule:
+ - cron: '30 5 * * *'
+ workflow_dispatch: # Allow manual trigger
+
+jobs:
+ test:
+ uses: ./.github/workflows/_test.yml
+ test_future:
+ uses: ./.github/workflows/_test_future.yml
+ build_package:
+ needs:
+ - test
+ uses: ./.github/workflows/_build_package.yml
+ build_and_publish_documentation:
+ needs:
+ - build_package
+ uses: ./.github/workflows/_build_and_publish_documentation.yml
diff --git a/.github/workflows/publish_release.yml b/.github/workflows/publish_release.yml
new file mode 100644
index 0000000..3075fab
--- /dev/null
+++ b/.github/workflows/publish_release.yml
@@ -0,0 +1,33 @@
+name: Publish Release
+run-name: Publish Release ${{ github.event.ref }} created by @${{ github.actor }}
+
+on:
+ push:
+ tags:
+ - v*
+
+jobs:
+ build_package:
+ uses: ./.github/workflows/_build_package.yml
+ # publish_package:
+ # name: Publish package
+ # needs:
+ # - build_package
+ # runs-on: ubuntu-latest
+ # environment: release
+ # permissions:
+ # id-token: write
+ # steps:
+ # - name: Download build artifacts
+ # uses: actions/download-artifact@v5
+ # with:
+ # name: dist
+ # path: dist
+ # - name: Publish to PyPI
+ # uses: pypa/gh-action-pypi-publish@release/v1
+ # # with: # Uncomment this line to publish to testpypi
+ # # repository-url: https://test.pypi.org/legacy/ # Uncomment this line to publish to testpypi
+ merge_into_release:
+ uses: ./.github/workflows/_merge_into_release.yml
+ secrets:
+ RELEASE_TOKEN: ${{ secrets.RELEASE_TOKEN }}
diff --git a/.github/workflows/pull_request_to_main.yml b/.github/workflows/pull_request_to_main.yml
new file mode 100644
index 0000000..1e73c39
--- /dev/null
+++ b/.github/workflows/pull_request_to_main.yml
@@ -0,0 +1,29 @@
+name: Pull Request to main
+run-name: Pull Request to main from ${{ github.event.pull_request.head.ref }} by @${{ github.actor }}
+
+on:
+ pull_request:
+ types:
+ - opened
+# - synchronize
+ - reopened
+ - ready_for_review
+ - converted_to_draft
+ branches:
+ - main
+ workflow_dispatch: # Allow manual trigger
+
+concurrency:
+ group: pr-${{ github.ref }}-1
+ cancel-in-progress: true
+
+jobs:
+ code_quality:
+ uses: ./.github/workflows/_code_quality.yml
+ test:
+ uses: ./.github/workflows/_test.yml
+# build_package:
+# needs:
+# - code_quality
+# - test
+# uses: ./.github/workflows/_build_package.yml
diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
new file mode 100644
index 0000000..08debf2
--- /dev/null
+++ b/.github/workflows/push.yml
@@ -0,0 +1,18 @@
+name: Push to custom branches
+run-name: Push to ${{ github.ref }} by @${{ github.actor }}
+
+on:
+ push:
+ branches-ignore:
+ - main
+ - release
+
+concurrency:
+ group: push-${{ github.ref }}-1
+ cancel-in-progress: true
+
+jobs:
+ code_quality:
+ uses: ./.github/workflows/_code_quality.yml
+ test:
+ uses: ./.github/workflows/_test.yml
diff --git a/.github/workflows/push_to_main.yml b/.github/workflows/push_to_main.yml
new file mode 100644
index 0000000..735f70b
--- /dev/null
+++ b/.github/workflows/push_to_main.yml
@@ -0,0 +1,26 @@
+name: Push to main
+run-name: Push to main by @${{ github.actor }}
+
+on:
+ push:
+ branches:
+ - main
+
+concurrency:
+ group: push-${{ github.ref }}-1
+ cancel-in-progress: true
+
+jobs:
+ code_quality:
+ uses: ./.github/workflows/_code_quality.yml
+ test:
+ uses: ./.github/workflows/_test.yml
+ build_package:
+ needs:
+ - code_quality
+ - test
+ uses: ./.github/workflows/_build_package.yml
+ build_and_publish_documentation:
+ needs:
+ - build_package
+ uses: ./.github/workflows/_build_and_publish_documentation.yml
diff --git a/.github/workflows/push_to_release.yml b/.github/workflows/push_to_release.yml
new file mode 100644
index 0000000..8880838
--- /dev/null
+++ b/.github/workflows/push_to_release.yml
@@ -0,0 +1,15 @@
+name: Push to release
+run-name: Push to release by @${{ github.actor }}
+
+on:
+ push:
+ branches:
+ - release
+
+concurrency:
+ group: push-${{ github.ref }}-1
+ cancel-in-progress: true
+
+jobs:
+ build_and_publish_documentation:
+ uses: ./.github/workflows/_build_and_publish_documentation.yml
diff --git a/.gitignore b/.gitignore
index ad4a1f1..71142fb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,3 @@
-# Created by https://www.toptal.com/developers/gitignore/api/python
-# Edit at https://www.toptal.com/developers/gitignore?templates=python
-
-### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
@@ -24,6 +20,7 @@ parts/
sdist/
var/
wheels/
+pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
@@ -74,6 +71,7 @@ instance/
# Sphinx documentation
docs/_build/
+_autosummary
# PyBuilder
.pybuilder/
@@ -87,31 +85,14 @@ profile_default/
ipython_config.py
# pyenv
-# For a library or package, you might want to ignore these files since the code is
-# intended to run in multiple environments; otherwise, check them in:
-# .python-version
+.python-version
-# pipenv
-# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# uv
+# It is generally recommended to include `uv.lock` in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
-# having no cross-platform support, pipenv may install dependencies that don't work, or not
-# install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-# This is especially recommended for binary packages to ensure reproducibility, and is more
-# commonly ignored for libraries.
-# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-# in version control.
-# https://pdm.fming.dev/#use-with-ide
-.pdm.toml
+# having no cross-platform support, `uv` might install dependencies in one environment that don't work in another.
+# In such case, `uv.lock` should be added to `.gitignore` (and, as such, excluded from version control).
+# uv.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
@@ -156,21 +137,24 @@ dmypy.json
# Cython debug symbols
cython_debug/
-# PyCharm
-# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-# and can be added to the global gitignore or merged into this file. For a more nuclear
-# option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
-
-### Python Patch ###
-# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
-poetry.toml
+# Ruff
+.ruff_cache
-# ruff
-.ruff_cache/
-
-# LSP config files
-pyrightconfig.json
-
-# End of https://www.toptal.com/developers/gitignore/api/python
+# PyCharm
+.idea
+
+# VS Code
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Inside /demos folder: ignore temporary logs, db's and local data files
+demos/**/*.log
+demos/**/*.db
+demos/**/*.nc
+
+# Cmake generated files
+CMakeUserPresets.json
diff --git a/.instructions.md b/.instructions.md
new file mode 100644
index 0000000..324c1a1
--- /dev/null
+++ b/.instructions.md
@@ -0,0 +1,59 @@
+# Project Instructions (Python)
+
+## Role
+You are a senior Python software engineer working in a regulated, enterprise environment.
+You prioritize correctness, reproducibility, readability and maintainability over cleverness.
+
+## Scope & Boundaries
+- Work only inside this repository.
+- Do NOT assume internet access unless explicitly stated.
+- Do NOT introduce hidden side effects (no shell hooks, no background tasks).
+- If something is ambiguous, ask for clarification before coding.
+
+## Coding Standards
+- Python >= 3.11
+- Follow PEP 8 and PEP 257
+- Prefer:
+ - `pathlib` over `os.path`
+ - `dataclasses` where appropriate
+ - explicit types (`typing`, `typing_extensions`)
+- Avoid premature abstractions.
+
+## Type Annotations
+- Type annotations are considered an important part of code quality and maintainability of this project.
+- Generally, use type annotations wherever possible, including return types.
+- Avoid using `Any` unless absolutely necessary. An exemption is type annotations for `*args` and `**kwargs`, which should be type annotated with `Any` by definition.
+- When a `# type: ignore` comment is no longer needed (e.g. because the root cause was fixed by adding stubs or correcting annotations), remove it. Stale suppressions hide real future errors. Use pyright's `reportUnnecessaryTypeIgnoreComment` and `uv run mypy --warn-unused-ignores` to detect them.
+- `# type: ignore` suppresses diagnostics for **both** mypy and pyright. `# pyright: ignore[rule]` suppresses only pyright. When mypy no longer needs a suppression but pyright still does, replace `# type: ignore` with `# pyright: ignore[rule]`. When both tools need suppression, keep `# type: ignore`.
+
+## Testing
+- Use `pytest`
+- Tests are required for:
+ - new functionality
+ - bug fixes
+- Tests must be deterministic and independent.
+- /tests/conftest.py contains shared fixtures and test configuration. Do not modify the existing fixtures therein without approval. You can, however, add new fixtures therein if you have a good reason.
+- /tests/test_working_directory is a folder that acts as the working directory for tests. Fixtures in /tests/conftest.py take care that CWD is changed to this folder for the duration of the entire test session, and that working directory is properly set up for each test, i.e. test artefacts created by tests get removed before the next test. You can add files and folders in /tests/test_working_directory as needed for testing, but do not modify or delete existing files without approval.
+
+## Documentation
+- Public functions, public classes and methods of public classes must have docstrings.
+- Modules should have a module-level docstring describing their purpose and usage.
+- Sub-packages should have an `__init__.py` with a docstring describing the sub-package.
+- Use numpy-style docstrings (numpydoc convention, as configured in `ruff.toml`).
+- When a class docstring already documents constructor parameters, the `__init__` docstring should be a brief summary (not duplicate the parameter list).
+- Do not use ambiguous Unicode characters in docstrings or comments (e.g. em-dash `—`, en-dash `–`). Use ASCII equivalents (e.g. hyphen `-`). Exemptions are characters listed in `allowed-confusables` in `ruff.toml`.
+- Update README or module docs if behavior or usage changes.
+
+## Tooling Assumptions
+- Package management: `uv`
+- Formatting: `ruff format`
+- Linting: `ruff check`
+- Type checking: `pyright` and `mypy`
+- Pre-commit hooks are set up for formatting and linting.
+
+## Agent Behavior
+- Prefer a **plan-first** approach.
+- Propose changes before implementing them.
+- Summarize changes and risks after implementation.
+- If you encounter an error, analyze the error message and stack trace to identify the root cause. Propose a fix before implementing it.
+- If you are unsure about how to proceed, ask for clarification before taking action.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index b04134c..8021673 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,11 +1,20 @@
----
repos:
- - repo: https://github.com/python/black
- rev: 22.3.0
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v6.0.0
hooks:
- - id: black
+ - id: mixed-line-ending
+ args: [--fix=auto]
+ - id: trailing-whitespace
+ - id: check-yaml
+ - id: check-toml
+ - id: check-merge-conflict
- repo: https://github.com/astral-sh/ruff-pre-commit
- # Ruff version.
- rev: v0.0.275
+ rev: v0.15.9
hooks:
- - id: ruff
+ - id: ruff-format
+ - id: ruff-check
+ # - repo: https://github.com/pre-commit/mirrors-mypy
+ # rev: v1.14.1
+ # hooks:
+ # - id: mypy
+exclude: '(.venv|.*_cache)/.*'
diff --git a/.prompt.md b/.prompt.md
new file mode 100644
index 0000000..3c31e6d
--- /dev/null
+++ b/.prompt.md
@@ -0,0 +1,29 @@
+## Task Context
+You are working on a codebase for a public Python package.
+The package is used in Python projects with primarily an engineering / research context.
+Assume the code may be used as evidence or input to regulated processes.
+
+## How to Work
+1. Restate the problem in your own words.
+2. Identify assumptions and constraints.
+3. Produce a clear, step-by-step plan.
+4. Wait for confirmation before implementing when running interactively. In non-interactive or batch contexts (e.g. CI, agentic pipelines), proceed autonomously unless the task scope is ambiguous or risky.
+5. Implement changes incrementally.
+6. Run tests and fix failures.
+7. Provide a concise summary of what changed.
+
+## Quality Bar
+- Correctness > Performance > Convenience
+- Clarity beats cleverness
+- Explicit is better than implicit
+
+## What NOT to Do
+- Do not auto-generate large refactors unless requested.
+- Do not change unrelated code “while you are there”.
+- Do not suppress warnings or errors without justification.
+
+## Output Expectations
+- Show diffs or clearly indicate modified files.
+- Explain non-obvious design decisions.
+- Call out any remaining risks, limitations, or TODOs.
+- Eventually, summarize the main changes in CHANGELOG.md. Add them to the "Unreleased" section. If the "Unreleased" section does not exist, create it. If there are already entries in the "Unreleased" section, append new entries below the existing ones.
diff --git a/.sourcery.yaml b/.sourcery.yaml
new file mode 100644
index 0000000..9543593
--- /dev/null
+++ b/.sourcery.yaml
@@ -0,0 +1,69 @@
+# 🪄 This is your project's Sourcery configuration file.
+
+# You can use it to get Sourcery working in the way you want, such as
+# ignoring specific refactorings, skipping directories in your project,
+# or writing custom rules.
+
+# 📚 For a complete reference to this file, see the documentation at
+# https://docs.sourcery.ai/Configuration/Project-Settings/
+
+# This file was auto-generated by Sourcery on 2023-02-22 at 11:42.
+
+version: '1' # The schema version of this config file
+
+ignore: # A list of paths or files which Sourcery will ignore.
+ - .git
+ - .venv
+ - .tox
+ - dist
+ - __pycache__
+ - docs/build
+
+rule_settings:
+ enable:
+ - default
+ disable: # A list of rule IDs Sourcery will never suggest.
+ - inline-immediately-returned-variable
+ rule_types:
+ - refactoring
+ - suggestion
+ - comment
+ python_version: '3.11' # A string specifying the lowest Python version your project supports. Sourcery will not suggest refactorings requiring a higher Python version.
+
+# rules: # A list of custom rules Sourcery will include in its analysis.
+# - id: no-print-statements
+# description: Do not use print statements in the test directory.
+# pattern: print(...)
+# language: python
+# replacement:
+# condition:
+# explanation:
+# paths:
+# include:
+# - test
+# exclude:
+# - conftest.py
+# tests: []
+# tags: []
+
+# rule_tags: {} # Additional rule tags.
+
+# metrics:
+# quality_threshold: 25.0
+
+# github:
+# labels: []
+# ignore_labels:
+# - sourcery-ignore
+# request_review: author
+# sourcery_branch: sourcery/{base_branch}
+
+# clone_detection:
+# min_lines: 3
+# min_duplicates: 2
+# identical_clones_only: false
+
+# proxy:
+# url:
+# ssl_certs_file:
+# no_ssl_verify: false
diff --git a/.vscode/extensions.json b/.vscode/extensions.json
new file mode 100644
index 0000000..d6549af
--- /dev/null
+++ b/.vscode/extensions.json
@@ -0,0 +1,21 @@
+{
+ // See https://go.microsoft.com/fwlink/?LinkId=827846 to learn about workspace recommendations.
+ // Extension identifier format: ${publisher}.${name}. Example: vscode.csharp
+ // List of extensions which should be recommended for users of this workspace.
+ "recommendations": [
+ "ms-python.python",
+ "ms-python.vscode-pylance",
+ "github.copilot-chat",
+ "ms-python.debugpy",
+ "ms-python.vscode-python-envs",
+ "charliermarsh.ruff",
+ "sourcery.sourcery",
+ "njpwerner.autodocstring",
+ "editorconfig.editorconfig",
+ "ms-toolsai.jupyter",
+ "ms-toolsai.jupyter-renderers",
+ "ms-python.mypy-type-checker",
+ ],
+ // List of extensions recommended by VS Code that should not be recommended for users of this workspace.
+ "unwantedRecommendations": []
+}
\ No newline at end of file
diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..31e4c23
--- /dev/null
+++ b/.vscode/launch.json
@@ -0,0 +1,81 @@
+{
+ // Use IntelliSense to learn about possible attributes.
+ // Hover to view descriptions of existing attributes.
+ // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+ "version": "0.2.0",
+ "configurations": [
+ {
+ "name": "Python: Debug Tests",
+ "type": "debugpy",
+ "request": "launch",
+ "program": "${file}",
+ "purpose": [
+ "debug-test"
+ ],
+ "console": "integratedTerminal",
+ "env": {
+ "PYTEST_ADDOPTS": "--no-cov"
+ },
+ "autoReload": {
+ "enable": true
+ },
+ "subProcess": true,
+ "justMyCode": true,
+ "redirectOutput": true, // pipe stdout/stderr to Debug Console
+ "showReturnValue": true,
+ "logToFile": false, // set true temporarily when diagnosing debugger issues
+ },
+ {
+ "name": "Python: Current File, cwd = file dir",
+ "type": "debugpy",
+ "request": "launch",
+ "cwd": "${fileDirname}", // working dir = dir where current file is
+ "program": "${file}",
+ "console": "integratedTerminal",
+ "autoReload": {
+ "enable": true
+ },
+ "subProcess": true,
+ "justMyCode": true,
+ "redirectOutput": true,
+ "showReturnValue": true,
+ "logToFile": false,
+ },
+ {
+ "name": "Python: Current File, cwd = workspace root folder",
+ "type": "debugpy",
+ "request": "launch",
+ "cwd": "${workspaceFolder}", // working dir = workspace root folder
+ "program": "${file}",
+ "console": "integratedTerminal",
+ "autoReload": {
+ "enable": true
+ },
+ "subProcess": true,
+ "justMyCode": true,
+ "redirectOutput": true,
+ "showReturnValue": true,
+ "logToFile": false,
+ },
+ {
+ "name": "crane_controller test_cli",
+ "type": "debugpy",
+ "request": "launch",
+ "cwd": "${workspaceFolder}\\tests\\test_working_directory",
+ "program": "${workspaceFolder}\\src\\crane_controller\\cli\\__main__.py",
+ "args": [
+ "test_config_file",
+ "-v",
+ ],
+ "console": "integratedTerminal",
+ "autoReload": {
+ "enable": true
+ },
+ "subProcess": true,
+ "justMyCode": true,
+ "redirectOutput": true,
+ "showReturnValue": true,
+ "logToFile": false,
+ },
+ ]
+}
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..efe9d30
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,52 @@
+{
+ "python.languageServer": "Pylance",
+ "editor.formatOnSave": true,
+ "notebook.formatOnSave.enabled": true,
+ "notebook.codeActionsOnSave": {
+ "notebook.source.fixAll": "explicit",
+ "notebook.source.organizeImports": "explicit",
+ },
+ "[python]": {
+ "editor.formatOnSave": true,
+ "editor.codeActionsOnSave": {
+ "source.fixAll": "always",
+ "source.organizeImports": "explicit",
+ },
+ "editor.defaultFormatter": "charliermarsh.ruff",
+ },
+ "autoDocstring.docstringFormat": "numpy",
+ "python.testing.unittestEnabled": false,
+ "python.testing.pytestEnabled": true,
+ "python.analysis.logLevel": "Warning",
+ "python.analysis.completeFunctionParens": false,
+ "python.analysis.diagnosticMode": "workspace",
+ "python.analysis.indexing": true,
+ "python.analysis.autoImportCompletions": true,
+ "python.analysis.inlayHints.variableTypes": false,
+ "python.analysis.inlayHints.functionReturnTypes": false,
+ "python.analysis.inlayHints.pytestParameters": true,
+ "python.terminal.executeInFileDir": true,
+ "python.terminal.activateEnvironment": false,
+ "python.terminal.activateEnvInCurrentTerminal": false,
+ "python.analysis.packageIndexDepths": [
+ {
+ "name": "pandas",
+ "depth": 4,
+ "includeAllSymbols": true,
+ },
+ {
+ "name": "matplotlib",
+ "depth": 4,
+ "includeAllSymbols": true,
+ },
+ {
+ "name": "mpl_toolkits",
+ "depth": 4,
+ "includeAllSymbols": true,
+ },
+ ],
+ "mypy-type-checker.importStrategy": "fromEnvironment",
+ "mypy-type-checker.reportingScope": "custom",
+ "mypy-type-checker.preferDaemon": true,
+ "ruff.configurationPreference": "filesystemFirst",
+}
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..0da1197
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,64 @@
+# Changelog
+
+All notable changes to the [crane-controller] project will be documented in this file.
+The changelog format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
+
+## [Unreleased]
+
+### Changed
+* CI: Skip slow `test_algorithm_strategies` test in GitHub workflow runs by adding a `slow` pytest marker and passing `-m "not slow"` in `_test.yml` and `_test_future.yml`
+* Adjusted and partly amended package structure to be in sync with latest changes in python_project_template v0.2.11
+* Typing:
+ * Added type annotations across all source, test, and script modules
+ * Added type stubs for `torch`, `matplotlib`, and `stable-baselines3`
+* Docstrings:
+ * Reformatted all existing docstrings to numpy-style
+ * Added missing docstrings across all source and script modules
+* Resolved all issues raised by `ruff`, `pyright`, and `mypy`
+
+### Fixed
+* Restored deterministic AntiPendulum environment seeding and aligned reset/step behavior with the existing environment tests.
+
+
+## [0.0.2] - YYYY-MM-DD
+
+### Changed
+* ...
+
+
+## [0.0.1] - YYYY-MM-DD
+
+* Initial release
+
+### Added
+
+* added this
+
+### Changed
+
+* changed that
+
+### Dependencies
+
+* updated to some_package_on_pypi>=0.1.0
+
+### Fixed
+
+* fixed issue #12345
+
+### Deprecated
+
+* following features will soon be removed and have been marked as deprecated:
+ * function x in module z
+
+### Removed
+
+* following features have been removed:
+ * function y in module z
+
+
+
+[unreleased]: https://github.com/dnv-opensource/crane-controller/compare/v0.0.2...HEAD
+[0.0.2]: https://github.com/dnv-opensource/crane-controller/compare/v0.0.1...v0.0.2
+[0.0.1]: https://github.com/dnv-opensource/crane-controller/releases/tag/v0.0.1
+[crane-controller]: https://github.com/dnv-opensource/crane-controller
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..7028660
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,36 @@
+title: crane-controller
+version: 0.0.1
+abstract: >-
+ AI-based control of a crane system using reinforcement learning.
+type: software
+authors:
+ - name: DNV AS
+ address: 'Veritasveien 1'
+ post-code: '1363'
+ city: Høvik
+ country: NO
+ website: 'https://www.dnv.com/'
+ - given-names: Siegfried
+ family-names: Eisinger
+ affiliation: DNV
+ email: siegfried.eisinger@dnv.com
+ website: 'https://www.linkedin.com/in/siegfried-eisinger-a337638b'
+ - given-names: Aleksandar
+ family-names: Babic
+ affiliation: DNV
+ email: aleksandar.babic@dnv.com
+ website: 'https://www.linkedin.com/in/aleksandar-babic-no'
+keywords:
+ - simulation
+ - model
+ - FMI
+ - OSP
+ - control algorithm
+ - crane
+ - anti-pendulum
+license: MIT
+license-url: 'https://dnv-opensource.github.io/crane-controller/LICENSE.html'
+url: 'https://dnv-opensource.github.io/crane-controller/README.html'
+repository-code: 'https://github.com/dnv-opensource/crane-controller'
+message: 'Please cite this software using these metadata.'
+cff-version: 1.2.0
diff --git a/LICENSE b/LICENSE
index 690e712..8f54c6d 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
-The MIT License
+MIT License
-Copyright (c) 2023 Farama Foundation
+Copyright (c) 2026 [DNV](https://www.dnv.com) [open source](https://github.com/dnv-opensource)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
@@ -9,13 +9,13 @@ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.rst b/README.rst
index 6d45f87..cf0fa4e 100644
--- a/README.rst
+++ b/README.rst
@@ -1,17 +1,22 @@
-Crane Controller
-================
+|pypi| |versions| |license| |ci| |docs|
-AI-based control of crane systems using reinforcement learning, developed at DNV AS.
+Introduction
+============
+
+The package provides AI-based control of a crane system using reinforcement learning, developed at DNV AS.
The primary goal is to solve the **anti-pendulum problem**: training an agent to dampen (or start)
the swing of a load hanging from a mobile crane, using only horizontal crane acceleration as the control input.
+Getting Started
+---------------
+
Environments
-------------
+^^^^^^^^^^^^
``AntiPendulumEnv``
The main environment. A mobile crane with a swinging load modelled via real crane physics
- (``py-crane`` library). The agent controls horizontal crane acceleration and must either
+ (``crane-controller`` library). The agent controls horizontal crane acceleration and must either
start or stop the pendulum motion.
- **Observation**: crane x-position, crane x-velocity, load polar angle, load x-velocity
@@ -22,7 +27,7 @@ Environments
A more general mobile crane environment for future work.
Algorithms
-----------
+^^^^^^^^^^
Three RL algorithms are implemented, each as a self-contained agent class:
@@ -39,7 +44,7 @@ Three RL algorithms are implemented, each as a self-contained agent class:
(3\ :sup:`4` combinations). Useful as a baseline.
Wrappers
---------
+^^^^^^^^
Generic Gymnasium wrappers (from the Farama Foundation examples) are included for reference:
@@ -49,7 +54,7 @@ Generic Gymnasium wrappers (from the Farama Foundation examples) are included fo
- ``ReacherRewardWrapper`` — weights multiple reward terms
Learning Examples
------------------
+^^^^^^^^^^^^^^^^^
Two classic Gymnasium environments were used as stepping stones when developing this project:
@@ -64,11 +69,14 @@ Installation
.. code-block:: shell
- cd crane-controller
- pip install -e .
+ pip install crane-controller
+
+
+Usage
+-----
Running
--------
+^^^^^^^
Install dependencies and run the test suite with ``uv``:
@@ -84,7 +92,7 @@ Test files are organised by algorithm:
Tests are suitable for CI/CD — no plot windows are produced.
Training
---------
+^^^^^^^^
**PPO:**
@@ -116,7 +124,7 @@ Key options:
- ``--dry-run`` — run 50 episodes with a reward plot and no model saved
Playing
--------
+^^^^^^^
Run a trained agent visually. Both scripts accept ``--render-mode`` with the following options:
@@ -139,7 +147,7 @@ Run a trained agent visually. Both scripts accept ``--render-mode`` with the fol
uv run python scripts/play_q.py --model-path tests/anti-pendulum.json --render-mode play-back --episodes 3
Analysing
----------
+^^^^^^^^^
Inspect a trained Q-table without running the environment:
@@ -156,9 +164,205 @@ specific states, use ``--obs`` with 5 integers (use ``-1`` as a wildcard):
The five observation dimensions are: ``[energy, pos, speed, distance, sector]``.
+Development Setup
+-----------------
+
+1. Install uv
+^^^^^^^^^^^^^
+This project uses `uv` as package manager.
+
+If you haven't already, install `uv `_, preferably using it's `"Standalone installer" `_ method:
+
+..on Windows:
+
+.. code:: sh
+
+ powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+
+..on MacOS and Linux:
+
+.. code:: sh
+
+ curl -LsSf https://astral.sh/uv/install.sh | sh
+
+(see `docs.astral.sh/uv `_ for all / alternative installation methods.)
+
+Once installed, you can update `uv` to its latest version, anytime, by running:
+
+``uv self update``
+
+2. Clone the repository
+^^^^^^^^^^^^^^^^^^^^^^^
+Clone the crane-controller repository into your local development directory:
+
+.. code:: sh
+
+ git clone https://github.com/dnv-opensource/crane-controller path/to/your/dev/crane-controller
+
+Change into the project directory after cloning:
+
+.. code:: sh
+
+ cd crane-controller
+
+3. Install dependencies
+^^^^^^^^^^^^^^^^^^^^^^^
+Run ``uv sync -U`` to create a virtual environment and install all project dependencies into it:
+
+.. code:: sh
+
+ uv sync -U
+
+..
+
+ **Note**: Using ``--no-dev`` will omit installing development
+ dependencies.
+
+ **Explanation**: The ``-U`` option stands for ``--update``. It forces
+ ``uv`` to fetch and install the latest versions of all dependencies,
+ ensuring that your environment is up-to-date.
+
+..
+
+ **Note**: ``uv`` will create a new virtual environment called
+ ``.venv`` in the project root directory when running ``uv sync -U``
+ the first time. Optionally, you can create your own virtual
+ environment using e.g. ``uv venv``, before running ``uv sync -U``.
+
+4. (Optional) Install CUDA support
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Run ``uv sync -U`` with option ``--extra cuda`` to in addition install
+torch with CUDA support:
+
+.. code:: sh
+
+ uv sync -U --extra cuda
+
+Alternatively, you can manually install torch with CUDA support. *Note
+1*: Do this preferably *after* running ``uv sync -U``. That way you
+ensure a virtual environment exists, which is a prerequisite before you
+install torch with CUDA support using below ``uv pip install`` command.
+
+To manually install torch with CUDA support, generate a
+``uv pip install`` command matching your local machine’s operating
+system using the wizard on the official `PyTorch
+website `__. *Note*: As we use
+``uv`` as package manager, remember to replace ``pip`` in the command
+generated by the wizard with ``uv pip``.
+
+If you are on Windows, the resulting ``uv pip install`` command will
+most likely look something like this:
+
+.. code:: sh
+
+ uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
+
+*Hint:* If you are unsure which cuda version to indicate in above
+``uv pip install .. /cuXXX`` command, you can use the shell command
+``nvidia-smi`` on your local system to find out the cuda version
+supported by the current graphics driver installed on your system. When
+then generating the ``uv pip install`` command with the wizard from the
+`PyTorch website `__, select
+the cuda version that matches the major version of what your graphics
+driver supports (major version must match, minor version may deviate).
+
+5. (Optional) Activate the virtual environment
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+When using ``uv``, there is in almost all cases no longer a need to manually activate the virtual environment.
+
+``uv`` will find the ``.venv`` virtual environment in the working directory or any parent directory, and activate it on the fly whenever you run a command via `uv` inside your project folder structure:
+
+.. code:: sh
+
+ uv run
+
+However, you still *can* manually activate the virtual environment if needed.
+When developing in an IDE, for instance, this can in some cases be necessary depending on your IDE settings.
+To manually activate the virtual environment, run one of the "known" legacy commands:
+
+..on Windows:
+
+.. code:: sh
+
+ .venv\Scripts\activate.bat
+
+..on Linux:
+
+.. code:: sh
+
+ source .venv/bin/activate
+
+6. Install pre-commit hooks
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+The ``.pre-commit-config.yaml`` file in the project root directory contains a configuration for pre-commit hooks.
+To install the pre-commit hooks defined therein in your local git repository, run:
+
+.. code:: sh
+
+ uv run pre-commit install
+
+All pre-commit hooks configured in ``.pre-commit-config.yam`` will now run each time you commit changes.
+
+pre-commit can also manually be invoked, at anytime, using:
+
+.. code:: sh
+
+ uv run pre-commit run --all-files
+
+To skip the pre-commit validation on commits (e.g. when intentionally
+committing broken code), run:
+
+.. code:: sh
+
+ uv run git commit -m --no-verify
+
+To update the hooks configured in ``.pre-commit-config.yaml`` to their
+newest versions, run:
+
+.. code:: sh
+
+ uv run pre-commit autoupdate
+
+7. Test that the installation works
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+To test that the installation works, run pytest in the project root folder:
+
+.. code:: sh
+
+ uv run pytest
+
+Meta
+----
+Copyright (c) 2026 `DNV `_ AS. All rights reserved.
+
+Siegfried Eisinger - `@LinkedIn `_ - siegfried.eisinger@dnv.com
+
+Aleksandar Babic - `@LinkedIn `_ - aleksandar.babic@dnv.com
+
+Distributed under the MIT license. See `LICENSE `_ for more information.
+
+`https://github.com/dnv-opensource/crane-controller `_
+
Contributing
------------
-- Fork this repository
-- Clone your fork
-- Set up pre-commit hooks: ``pre-commit install``
+1. Fork it ``_
+2. Create an issue in your GitHub repo
+3. Create your branch based on the issue number and type (``git checkout -b issue-name``)
+4. Evaluate and stage the changes you want to commit (``git add -i``)
+5. Commit your changes (``git commit -am 'place a descriptive commit message here'``)
+6. Push to the branch (``git push origin issue-name``)
+7. Create a new Pull Request in GitHub
+
+For your contribution, please make sure you follow the `STYLEGUIDE `_ before creating the Pull Request.
+
+.. |pypi| image:: https://img.shields.io/pypi/v/crane-controller.svg?color=blue
+ :target: https://pypi.python.org/pypi/crane-controller
+.. |versions| image:: https://img.shields.io/pypi/pyversions/crane-controller.svg?color=blue
+ :target: https://pypi.python.org/pypi/crane-controller
+.. |license| image:: https://img.shields.io/pypi/l/crane-controller.svg
+ :target: https://github.com/dnv-opensource/crane-controller/blob/main/LICENSE
+.. |ci| image:: https://img.shields.io/github/actions/workflow/status/dnv-opensource/crane-controller/.github%2Fworkflows%2Fnightly_build.yml?label=ci
+.. |docs| image:: https://img.shields.io/github/actions/workflow/status/dnv-opensource/crane-controller/.github%2Fworkflows%2Fpush_to_release.yml?label=docs
+ :target: https://dnv-opensource.github.io/crane-controller/README.html
diff --git a/STYLEGUIDE.md b/STYLEGUIDE.md
new file mode 100644
index 0000000..5b547b3
--- /dev/null
+++ b/STYLEGUIDE.md
@@ -0,0 +1,423 @@
+
+# Style Guide
+
+All code shall be [Ruff](https://docs.astral.sh/ruff) formatted.
+
+References, details as well as examples of bad/good styles and their respective reasoning can be found below.
+
+## References
+
+* [PEP-8](https://www.python.org/dev/peps/pep-0008/) (see also [pep8.org](https://pep8.org/))
+* [PEP-257](https://www.python.org/dev/peps/pep-0257/)
+* Python style guide by [theluminousmen.com](https://luminousmen.com/post/the-ultimate-python-style-guidelines)
+* [Documenting Python Code: A Complete Guide](https://realpython.com/documenting-python-code)
+* [Jupyter](https://jupyter.readthedocs.io/en/latest/contributing/ipython-dev-guide/coding_style.html) style guide
+* Python style guide on [learnpython.com](https://learnpython.com/blog/python-coding-best-practices-and-style-guidelines/)
+* [Ruff](https://docs.astral.sh/ruff)
+
+## Code Layout
+
+* Use 4 spaces instead of tabs
+* Maximum line length is 120 characters (not 79 as proposed in [PEP-8](https://www.python.org/dev/peps/pep-0008/))
+* 2 blank lines between classes and functions
+* 1 blank line within class, between class methods
+* Use blank lines for logic separation of functionality within functions/methods wherever it is justified
+* No whitespace adjacent to parentheses, brackets, or braces
+
+```py
+ # Bad
+ spam( items[ 1 ], { key1 : arg1, key2 : arg2 }, )
+
+ # Good
+ spam(items[1], {key1: arg1, key2: arg2}, [])
+```
+
+* Surround operators with single whitespace on either side.
+
+```py
+ # Bad
+ x<1
+
+ # Good
+ x == 1
+```
+
+* Never end your lines with a semicolon, and do not use a semicolon to put two statements on the same line
+* When branching, always start a new block on a new line
+
+```py
+ # Bad
+ if flag: return None
+
+ # Good
+ if flag:
+ return None
+```
+
+* Similarly to branching, do not write methods on one line in any case:
+
+```py
+ # Bad
+ def do_something(self): print("Something")
+
+ # Good
+ def do_something(self):
+ print("Something")
+```
+
+* Place a class's `__init__` function (the constructor) always at the beginning of the class
+
+## Line Breaks
+
+* If function arguments do not fit into the specified line length, move them to a new line with indentation
+
+```py
+ # Bad
+ def long_function_name(var_one, var_two, var_three,
+ var_four):
+ print(var_one)
+
+ # Bad
+ def long_function_name(var_one, var_two, var_three,
+ var_four):
+ print(var_one)
+
+ # Better (but not preferred)
+ def long_function_name(var_one,
+ var_two,
+ var_three,
+ var_four):
+ print(var_one)
+
+ # Good (and preferred)
+ def long_function_name(
+ var_one,
+ var_two,
+ var_three,
+ var_four,
+ ):
+ print(var_one)
+```
+
+* Move concatenated logical conditions to new lines if the line does not fit the maximum line size. This will help you understand the condition by looking from top to bottom. Poor formatting makes it difficult to read and understand complex predicates.
+
+```py
+ # Good
+ if (
+ this_is_one_thing
+ and that_is_another_thing
+ or that_is_third_thing
+ or that_is_yet_another_thing
+ and one_more_thing
+ ):
+ do_something()
+```
+
+* Where binary operations stretch multiple lines, break lines before the binary operators, not thereafter
+
+```py
+ # Bad
+ GDP = (
+ private_consumption +
+ gross_investment +
+ government_investment +
+ government_spending +
+ (exports - imports)
+ )
+
+ # Good
+ GDP = (
+ private_consumption
+ + gross_investment
+ + government_investment
+ + government_spending
+ + (exports - imports)
+ )
+```
+
+* Chaining methods should be broken up on multiple lines for better readability
+
+```py
+ (
+ df.write.format("jdbc")
+ .option("url", "jdbc:postgresql:dbserver")
+ .option("dbtable", "schema.tablename")
+ .option("user", "username")
+ .option("password", "password")
+ .save()
+ )
+```
+
+* Add a trailing comma to sequences of items when the closing container token ], ), or } does not appear on the same line as the final element
+
+```py
+ # Bad
+ y = [
+ 0,
+ 1,
+ 4,
+ 6
+ ]
+ z = {
+ 'a': 1,
+ 'b': 2
+ }
+
+ # Good
+ x = [1, 2, 3]
+
+ # Good
+ y = [
+ 0,
+ 1,
+ 4,
+ 6, <- note the trailing comma
+ ]
+ z = {
+ 'a': 1,
+ 'b': 2, <- note the trailing comma
+ }
+```
+
+## String Formatting
+
+* When quoting string literals, use double-quoted strings. When the string itself contains single or double quote characters, however, use the respective other one to avoid backslashes in the string. It improves readability.
+* Use f-strings to format strings:
+
+```py
+ # Bad
+ print("Hello, %s. You are %s years old. You are a %s." % (name, age, profession))
+
+ # Good
+ print(f"Hello, {name}. You are {age} years old. You are a {profession}.")
+```
+
+* Use multiline strings, not \ , since it gets much more readable.
+
+```py
+ raise AttributeError(
+ "Here is a multiline error message with a very long first line "
+ "and a shorter second line."
+ )
+```
+
+## Naming Conventions
+
+* For module names: `lowercase` .
+Long module names can have words separated by underscores (`really_long_module_name.py`), but this is not required. Try to use the convention of nearby files.
+* For class names: `CamelCase`
+* For methods, functions, variables and attributes: `lowercase_with_underscores`
+* For constants: `UPPERCASE` or `UPPERCASE_WITH_UNDERSCORES`
+(Python does not differentiate between variables and constants. Using UPPERCASE for constants is just a convention, but helps a lot to quickly identify variables meant to serve as constants.)
+* Implementation-specific private methods and variables will use `_single_underscore_prefix`
+* Don't include the type of a variable in its name.
+ E.g. use `senders` instead of `sender_list`
+* Names shall be clear about what a variable, class, or function contains or does. If you struggle to come up with a clear name, rethink your architecture: Often, the difficulty in finding a crisp name for something is a hint that separation of responsibilities can be improved. The solution then is less to agree on a name, but to start a round of refactoring: The name you're seeking often comes naturally then with refactoring to an improved architecture with clear responsibilities.
+(see [SRP](https://en.wikipedia.org/wiki/Single-responsibility_principle), Single-Responsibilty Principle by Robert C. Martin)
+
+## Named Arguments
+
+* Use named arguments to improve readability and avoid mistakes introduced with future code maintenance
+
+```py
+ # Bad
+ urlget("[http://google.com](http://google.com/)", 20)
+
+ # Good
+ urlget("[http://google.com](http://google.com/)", timeout=20)
+```
+
+* Never use mutable objects as default arguments in Python. If an attribute in a class or a named parameter in a function is of a mutable data type (e.g. a list or dict), never set its default value in the declaration of an object but always set it to None first, and then only later assign the default value in the class's constructor, or the functions body, respectively. Sounds complicated? If you prefer the shortcut, the examples below are your friend.
+If you are interested in the long story including the why‘s, read these discussions on [Reddit](https://old.reddit.com/r/Python/comments/opb7hm/do_not_use_mutable_objects_as_default_arguments/) and [Twitter](https://twitter.com/willmcgugan/status/1419616480971399171).
+
+```py
+ # Bad
+ class Foo:
+ items = []
+
+ # Good
+ class Foo:
+ items = None
+ def __init__(self):
+ self.items = []
+
+
+ # Bad
+ class Foo:
+ def __init__(self, items=[]):
+ self.items = items
+
+ # Good
+ class Foo:
+ def __init__(self, items=None):
+ self.items = items or []
+
+
+ # Bad
+ def some_function(x, y, items=[]):
+ ...
+
+ # Good
+ def some_function(x, y, items=None):
+ items = items or []
+ ...
+```
+
+## Commenting
+
+* First of all, if the code needs comments to clarify its work, you should think about refactoring it. The best comment to code is the code itself.
+* Describe complex, possibly incomprehensible points and side effects in the comments
+* Separate `#` and the comment with one whitespace
+
+```py
+ #bad comment
+ # good comment
+```
+
+* Use inline comments sparsely
+* Where used, inline comments shall have 2 whitespaces before the `#` and one whitespace thereafter
+
+```py
+ x = y + z # inline comment
+ str1 = str2 + str3 # another inline comment
+```
+
+* If a piece of code is poorly understood, mark the piece with a `@TODO:` tag and your name to support future refactoring:
+
+```py
+ def get_ancestors_ids(self):
+ # @TODO: Do a cache reset while saving the category tree. CLAROS, YYYY-MM-DD
+ cache_name = f"{self._meta.model_name}_ancestors_{self.pk}"
+ cached_ids = cache.get(cache_name)
+ if cached_ids:
+ return cached_ids
+
+ ids = [c.pk for c in self.get_ancestors(include_self=True)]
+ cache.set(cache_name, ids, timeout=3600)
+
+ return ids
+```
+
+## Type hints
+
+* Use type hints in function signatures and module-scope variables. This is good documentation and can be used with linters for type checking and error checking. Use them whenever possible.
+* Use pyi files to type annotate third-party or extension modules.
+
+## Docstrings
+
+* All Docstrings should be written in [Numpy](https://numpydoc.readthedocs.io/en/latest/format.html) format. For a good tutorial on Docstrings, see [Documenting Python Code: A Complete Guide](https://realpython.com/documenting-python-code)
+* In a Docstring, summarize function/method behavior and document its arguments, return value(s), side effects, exceptions raised, and restrictions
+* Wrap Docstrings with triple double quotes (""")
+* The description of the arguments must be indented
+
+```py
+ def some_method(name, print=False):
+ """This function does something
+
+ Parameters
+ ----------
+ name : str
+ The name to use
+ print: bool, optional
+ A flag used to print the name to the console, by default False
+
+ Raises
+ ------
+ KeyError
+ If name is not found
+
+ Returns
+ -------
+ int
+ The return code
+ """
+ ...
+ return 0
+```
+
+## Exceptions
+
+* Raise specific exceptions and catch specific exceptions, such as KeyError, ValueError, etc.
+* Do not raise or catch just Exception, except in rare cases where this is unavoidable, such as a try/except block on the top-level loop of some long-running process. For a good tutorial on why this matters, see [The Most Diabolical Python Antipattern](https://realpython.com/the-most-diabolical-python-antipattern/).
+* Minimize the amount of code in a try/except block. The larger the body of the try,
+ the more likely that an exception will be raised by a line of code that you didn’t expect to raise an exception.
+
+## Imports
+
+* Avoid creating circular imports by importing modules more specialized than the one you are editing
+* Relative imports are forbidden ([PEP-8](https://www.python.org/dev/peps/pep-0008/) only “highly discourages” them). Where absolutely needed, the `from future import absolute_import` syntax should be used (see [PEP-328](https://www.python.org/dev/peps/pep-0328/))
+* Never use wildcard imports (`from import *`). Always be explicit about what you're importing. Namespaces make code easier to read, so use them.
+* Break long imports using parentheses and indent by 4 spaces. Include the trailing comma after the last import and place the closing bracket on a separate line
+
+```py
+ from my_pkg.utils import (
+ some_utility_method_1,
+ some_utility_method_2,
+ some_utility_method_3,
+ some_utility_method_4,
+ some_utility_method_5,
+ )
+```
+
+* Imports should be written in the following order, separated by a blank line:
+ 1. build-in modules
+ 2. third-party modules
+ 3. local application/library specific imports
+
+```py
+ import logging
+ import os
+ import typing as T
+
+ import pandas as pd
+ import numpy as np
+
+ import crane_controller
+ import crane_controller.my_module
+ from crane_controller.my_module import my_function, MyClass
+```
+
+* Even if a Python file is intended to be used as executable / script file only, it shall still be importable as a module, and its import should not have any side effects. Its main functionality shall hence be in a `main()` function, so that the code can be imported as a module for testing or being reused in the future:
+
+```py
+ def main():
+ ...
+
+ if __name__ == "__main__":
+ main()
+```
+
+## Unit-tests
+
+* Use pytest as the preferred testing framework.
+* The name of a test shall clearly express what is being tested.
+* Each test should preferably check only one specific aspect.
+
+```py
+ # Bad
+ def test_smth():
+ result = f()
+ assert isinstance(result, list)
+ assert result[0] == 1
+ assert result[1] == 2
+ assert result[2] == 3
+ assert result[3] == 4
+
+ # Good
+ def test_smth_type():
+ result = f()
+ assert isinstance(result, list), "Result should be list"
+
+ def test_smth_values():
+ result = f()
+ assert set(result) == set(expected), f"Result should be {set(expected)}"
+```
+
+## And finally: It is a bad idea to use
+
+* global variables.
+* iterators where they can be replaced by vectorized operations.
+* lambda where it is not required.
+* map and lambda where it can be replaced by a simple list comprehension.
+* multiple nested maps and lambdas.
+* nested functions. They are hard to test and debug.
diff --git a/docs/_ext/assumption.py b/docs/_ext/assumption.py
deleted file mode 100644
index ed36cdf..0000000
--- a/docs/_ext/assumption.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from docutils import nodes # type: ignore
-from docutils.parsers.rst import Directive # type: ignore
-from sphinx.locale import _
-from sphinx.util.docutils import SphinxDirective
-
-
-class Assumption(nodes.Admonition, nodes.Element):
- pass
-
-
-class AssumptionList(nodes.General, nodes.Element):
- pass
-
-
-def visit_assumption_node(self, node):
- self.visit_admonition(node)
-
-
-def depart_assumption_node(self, node):
- self.depart_admonition(node)
-
-
-class AssumptionlistDirective(Directive):
- def run(self):
- return [AssumptionList("")]
-
-
-class AssumptionDirective(SphinxDirective):
- # this enables content in the directive
- has_content = True
-
- def run(self):
- targetid = "assumption-%d" % self.env.new_serialno("assumption")
- targetnode = nodes.target("", "", ids=[targetid])
-
- assumption_node = Assumption("\n".join(self.content))
- assumption_node += nodes.title(_("Assumption"), _("Assumption"))
- self.state.nested_parse(self.content, self.content_offset, assumption_node)
-
- if not hasattr(self.env, "assumption_all_assumptions"):
- self.env.assumptions_all_assumptions = [] # type: ignore[attr-defined]
-
- self.env.assumptions_all_assumptions.append( # type: ignore[attr-defined]
- {
- "docname": self.env.docname,
- "lineno": self.lineno,
- "assumption": assumption_node.deepcopy(),
- "target": targetnode,
- }
- )
-
- return [targetnode, assumption_node]
-
-
-def purge_assumptions(app, env, docname):
- if not hasattr(env, "assumption_all_assumptions"):
- return
-
- env.assumption_all_assumptions = [
- assumption for assumption in env.assumption_all_assumptions if assumption["docname"] != docname
- ]
-
-
-def merge_assumptions(app, env, docnames, other):
- if not hasattr(env, "assumption_all_assumptions"):
- env.assumption_all_assumptions = []
- if hasattr(other, "assumption_all_assumptions"):
- env.assumption_all_assumptions.extend(other.assumption_all_assumptions)
-
-
-def process_assumption_nodes(app, doctree, fromdocname):
- if not app.config.assumption_include_assumptions:
- for node in doctree.findall(Assumption):
- node.parent.remove(node)
-
- # Replace all assumptionlist nodes with a list of the collected assumptions.
- # Augment each assumption with a backlink to the original location.
- env = app.builder.env
-
- if not hasattr(env, "assumption_all_assumptions"):
- env.assumption_all_assumptions = []
-
- for node in doctree.findall(AssumptionList):
- if not app.config.assumption_include_assumptions:
- node.replace_self([])
- continue
-
- content = []
-
- for assumption_info in env.assumption_all_assumptions:
- para = nodes.paragraph()
- filename = env.doc2path(assumption_info["docname"], base=None)
- description = _("(The original entry is located in %s, line %d and can be found ") % (
- filename,
- assumption_info["lineno"],
- )
- para += nodes.Text(description)
-
- # Create a reference
- newnode = nodes.reference("", "")
- innernode = nodes.emphasis(_("here"), _("here"))
- newnode["refdocname"] = assumption_info["docname"]
- newnode["refuri"] = app.builder.get_relative_uri(fromdocname, assumption_info["docname"])
- newnode["refuri"] += "#" + assumption_info["target"]["refid"]
- newnode.append(innernode)
- para += newnode
- para += nodes.Text(".)")
-
- # Insert into the assumptionlist
- content.append(assumption_info["assumption"])
- content.append(para)
-
- node.replace_self(content)
-
-
-def setup(app):
- app.add_config_value("assumption_include_assumptions", True, "html")
-
- app.add_node(AssumptionList)
- app.add_node(
- Assumption,
- html=(visit_assumption_node, depart_assumption_node),
- latex=(visit_assumption_node, depart_assumption_node),
- text=(visit_assumption_node, depart_assumption_node),
- )
-
- app.add_directive("assumption", AssumptionDirective)
- app.add_directive("assumptionlist", AssumptionlistDirective)
- app.connect("doctree-resolved", process_assumption_nodes)
- app.connect("env-purge-doc", purge_assumptions)
- app.connect("env-merge-info", merge_assumptions)
-
- return {
- "version": "0.1",
- "parallel_read_safe": True,
- "parallel_write_safe": True,
- }
diff --git a/docs/_ext/get_from_code.py b/docs/_ext/get_from_code.py
deleted file mode 100644
index 7185031..0000000
--- a/docs/_ext/get_from_code.py
+++ /dev/null
@@ -1,68 +0,0 @@
-"""Module introducing the Sphinx extension get-from-code, fetching docstrings, string representations, ... from code."""
-
-import importlib
-import logging
-
-from docutils import nodes # type: ignore
-
-# from docutils.parsers.rst import Directive
-from sphinx.util.docutils import SphinxDirective
-
-logger = logging.getLogger(__name__)
-
-
-class GetFromCode(SphinxDirective):
- # this enables content in the directive
- required_arguments = 1
- optional_arguments = 1
- has_content = True
-
- def run(self):
- """Extract the value of the (constant) variable or the docstring according to spec."""
- if len(self.arguments) == 1:
- self.typ = None
- else:
- self.typ = self.arguments[1]
- sPkg, _, sObj = self.arguments[0].partition(".")
- sMod, _, sObj = sObj.partition(".")
- obj = importlib.import_module(sPkg + "." + sMod)
- while len(sObj):
- sSub, _, sObj = sObj.partition(".")
- obj = getattr(obj, sSub)
- assert obj is not None, f"Attribute {sSub} of {obj} not found"
- logger.debug(
- "INFO [" + self.arguments[0] + "] Type:",
- type(obj),
- ", __name__: " + type(obj).__name__,
- ", type: ",
- self.typ,
- )
- if self.typ is not None:
- if self.typ == "code":
- text = (
- obj.__code__
- ) # TODO: this is only the code object. Need to retrieve the code text and format it appropriately
- else:
- text = "Type " + self.typ + " not yet implemented"
- elif isinstance(obj, (complex, float, int, str, list, dict, tuple)): # no docstring
- text = str(obj).strip()
- elif isinstance(obj.__doc__, str):
- text = obj.__doc__.strip()
- else:
- raise NotImplementedError(f"Unhandled situation obj:{obj}") from None
-
- if self.typ is None: # need to parse the extracted text
- parNode = nodes.paragraph(text="") # use an empty paragraph node as parent
- self.state.nested_parse(self.content, 0, parNode) # here the content of the retrieved text is parsed
- else: # use the text as is
- parNode = nodes.paragraph(text=text)
- return [parNode]
-
-
-def setup(app):
- app.add_directive("get-from-code", GetFromCode)
- return {
- "version": "0.1",
- "parallel_read_safe": True,
- "parallel_write_safe": True,
- }
diff --git a/docs/_ext/spec.py b/docs/_ext/spec.py
deleted file mode 100644
index cd0e664..0000000
--- a/docs/_ext/spec.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""Extension for 'spec-type' Sphinx directives
-`Build along the lines of the tutorial `_
-With base extension 'spec' and sub-classes 'assumption', 'limitation' and 'requirement'. These just change the title of the admonition.
-"""
-
-from docutils import nodes # type: ignore
-from docutils.parsers.rst import Directive # type: ignore
-from sphinx.locale import _
-from sphinx.util.docutils import SphinxDirective
-
-
-class Spec(nodes.Admonition, nodes.Element):
- pass
-
-
-class SpecList(nodes.General, nodes.Element):
- pass
-
-
-def visit_spec_node(self, node):
- self.visit_admonition(node)
-
-
-def depart_spec_node(self, node):
- self.depart_admonition(node)
-
-
-class SpeclistDirective(Directive):
- def run(self):
- return [SpecList("")]
-
-
-class SpecDirective(SphinxDirective):
- _title = "Spec"
- # this enables content in the directive
- has_content = True
-
- def run(self):
- targetid = "spec-%d" % self.env.new_serialno("spec")
- targetnode = nodes.target("", "", ids=[targetid])
-
- spec_node = Spec("\n".join(self.content))
- spec_node += nodes.title(_("Spec"), _(self._title))
- self.state.nested_parse(self.content, self.content_offset, spec_node)
-
- if not hasattr(self.env, "spec_all_specs"):
- self.env.spec_all_specs = [] # type: ignore[attr-defined]
-
- self.env.spec_all_specs.append( # type: ignore[attr-defined]
- {
- "docname": self.env.docname,
- "lineno": self.lineno,
- "spec": spec_node.deepcopy(),
- "target": targetnode,
- }
- )
-
- return [targetnode, spec_node]
-
-
-class LimitationDirective(SpecDirective):
- _title = "Limitation"
-
-
-class AssumptionDirective(SpecDirective):
- _title = "Assumption"
-
-
-class RequirementDirective(SpecDirective):
- _title = "Requirement"
-
-
-def purge_specs(app, env, docname):
- if not hasattr(env, "spec_all_specs"):
- return
-
- env.spec_all_specs = [spec for spec in env.spec_all_specs if spec["docname"] != docname]
-
-
-def merge_specs(app, env, docnames, other):
- if not hasattr(env, "spec_all_specs"):
- env.spec_all_specs = []
- if hasattr(other, "spec_all_specs"):
- env.spec_all_specs.extend(other.spec_all_specs)
-
-
-def process_spec_nodes(app, doctree, fromdocname):
- if not app.config.spec_include_specs:
- for node in doctree.findall(Spec):
- node.parent.remove(node)
-
- # Replace all speclist nodes with a list of the collected specs.
- # Augment each spec with a backlink to the original location.
- env = app.builder.env
-
- if not hasattr(env, "spec_all_specs"):
- env.spec_all_specs = []
-
- for node in doctree.findall(SpecList):
- if not app.config.spec_include_specs:
- node.replace_self([])
- continue
-
- content = []
-
- for spec_info in env.spec_all_specs:
- para = nodes.paragraph()
- filename = env.doc2path(spec_info["docname"], base=None)
- description = _("(The original entry is located in %s, line %d and can be found ") % (
- filename,
- spec_info["lineno"],
- )
- para += nodes.Text(description)
-
- # Create a reference
- newnode = nodes.reference("", "")
- innernode = nodes.emphasis(_("here"), _("here"))
- newnode["refdocname"] = spec_info["docname"]
- newnode["refuri"] = app.builder.get_relative_uri(fromdocname, spec_info["docname"])
- newnode["refuri"] += "#" + spec_info["target"]["refid"]
- newnode.append(innernode)
- para += newnode
- para += nodes.Text(".)")
-
- # Insert into the speclist
- content.append(spec_info["spec"])
- content.append(para)
-
- node.replace_self(content)
-
-
-def setup(app):
- app.add_config_value("spec_include_specs", False, "html")
-
- app.add_node(SpecList)
- app.add_node(
- Spec,
- html=(visit_spec_node, depart_spec_node),
- latex=(visit_spec_node, depart_spec_node),
- text=(visit_spec_node, depart_spec_node),
- )
-
- app.add_directive("spec", SpecDirective)
- app.add_directive("assumption", AssumptionDirective)
- app.add_directive("limitation", LimitationDirective)
- app.add_directive("requirement", RequirementDirective)
- app.add_directive("speclist", SpeclistDirective)
- app.connect("doctree-resolved", process_spec_nodes)
- app.connect("env-purge-doc", purge_specs)
- app.connect("env-merge-info", merge_specs)
-
- return {
- "version": "0.1",
- "parallel_read_safe": True,
- "parallel_write_safe": True,
- }
diff --git a/docs/_ext/specification.py b/docs/_ext/specification.py
deleted file mode 100644
index e317425..0000000
--- a/docs/_ext/specification.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from docutils import nodes # type: ignore
-from docutils.parsers.rst import Directive # type: ignore
-from sphinx.locale import _
-from sphinx.util.docutils import SphinxDirective
-
-
-class Spec(nodes.Admonition, nodes.Element):
- pass
-
-
-class SpecList(nodes.General, nodes.Element):
- pass
-
-
-def visit_spec_node(self, node):
- self.visit_admonition(node)
-
-
-def depart_spec_node(self, node):
- self.depart_admonition(node)
-
-
-class SpeclistDirective(Directive):
- def run(self):
- return [SpecList("")]
-
-
-class SpecDirective(SphinxDirective):
- # this enables content in the directive
- optional_arguments = 1
- has_content = True
-
- def run(self):
- print("ARGS", self.arguments)
-
- targetid = "spec-%d" % self.env.new_serialno("spec")
- targetnode = nodes.target("", "", ids=[targetid])
-
- spec_node = Spec("\n".join(self.content))
- spec_node += nodes.title(_("Spec"), _("Spec"))
- self.state.nested_parse(self.content, self.content_offset, spec_node)
-
- if not hasattr(self.env, "spec_all_specs"):
- self.env.specs_all_specs = [] # type: ignore[attr-defined]
-
- self.env.specs_all_specs.append( # type: ignore[attr-defined]
- {
- "docname": self.env.docname,
- "lineno": self.lineno,
- "spec": spec_node.deepcopy(),
- "target": targetnode,
- }
- )
-
- return [targetnode, spec_node]
-
-
-def purge_specs(app, env, docname):
- if not hasattr(env, "spec_all_specs"):
- return
-
- env.spec_all_specs = [spec for spec in env.spec_all_specs if spec["docname"] != docname]
-
-
-def merge_specs(app, env, docnames, other):
- if not hasattr(env, "spec_all_specs"):
- env.spec_all_specs = []
- if hasattr(other, "spec_all_specs"):
- env.spec_all_specs.extend(other.spec_all_specs)
-
-
-def process_spec_nodes(app, doctree, fromdocname):
- if not app.config.spec_include_specs:
- for node in doctree.findall(Spec):
- node.parent.remove(node)
-
- # Replace all speclist nodes with a list of the collected specs.
- # Augment each spec with a backlink to the original location.
- env = app.builder.env
-
- if not hasattr(env, "spec_all_specs"):
- env.spec_all_specs = []
-
- for node in doctree.findall(SpecList):
- if not app.config.spec_include_specs:
- node.replace_self([])
- continue
-
- content = []
-
- for spec_info in env.spec_all_specs:
- para = nodes.paragraph()
- filename = env.doc2path(spec_info["docname"], base=None)
- description = _("(The original entry is located in %s, line %d and can be found ") % (
- filename,
- spec_info["lineno"],
- )
- para += nodes.Text(description)
-
- # Create a reference
- newnode = nodes.reference("", "")
- innernode = nodes.emphasis(_("here"), _("here"))
- newnode["refdocname"] = spec_info["docname"]
- newnode["refuri"] = app.builder.get_relative_uri(fromdocname, spec_info["docname"])
- newnode["refuri"] += "#" + spec_info["target"]["refid"]
- newnode.append(innernode)
- para += newnode
- para += nodes.Text(".)")
-
- # Insert into the speclist
- content.append(spec_info["spec"])
- content.append(para)
-
- node.replace_self(content)
-
-
-def setup(app):
- app.add_config_value("spec_include_specs", True, "html")
-
- app.add_node(SpecList)
- app.add_node(
- Spec,
- html=(visit_spec_node, depart_spec_node),
- latex=(visit_spec_node, depart_spec_node),
- text=(visit_spec_node, depart_spec_node),
- )
-
- app.add_directive("spec", SpecDirective)
- app.add_directive("speclist", SpeclistDirective)
- app.connect("doctree-resolved", process_spec_nodes)
- app.connect("env-purge-doc", purge_specs)
- app.connect("env-merge-info", merge_specs)
-
- return {
- "version": "0.1",
- "parallel_read_safe": True,
- "parallel_write_safe": True,
- }
diff --git a/docs/source/_static/py_crane.svg b/docs/source/_static/crane-controller.svg
similarity index 100%
rename from docs/source/_static/py_crane.svg
rename to docs/source/_static/crane-controller.svg
diff --git a/docs/source/api.rst b/docs/source/api.rst
index 95a6cb4..656dff5 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -4,4 +4,4 @@ API Reference
.. toctree::
:maxdepth: 4
- py_crane
+ crane_controller
diff --git a/docs/assurance.pdf b/docs/source/assurance.pdf
similarity index 100%
rename from docs/assurance.pdf
rename to docs/source/assurance.pdf
diff --git a/docs/assurance.rst b/docs/source/assurance.rst
similarity index 93%
rename from docs/assurance.rst
rename to docs/source/assurance.rst
index f471bb7..9d67d44 100644
--- a/docs/assurance.rst
+++ b/docs/source/assurance.rst
@@ -3,11 +3,11 @@ Assurance case notes
Concept
-------
-* Control the crane within
+* Control the crane within
- the stated environmental limits
- the stated control limits
* 'crane' refers to simulator or real crane
- - simulator should be assured with respect to
+ - simulator should be assured with respect to
realistically simulate both crane and environment,
so that
@@ -25,7 +25,7 @@ Crane limitations
Environment simulator
The environmental capabilities shall be specified.
-
+
#. crane mounted on fixed structure. External forces directly specified
#. crane mounted on moveable structure ...
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 624ae7e..53b4c1a 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -15,18 +15,17 @@
from pathlib import Path
sys.path.insert(0, str(Path("../../src").absolute()))
-sys.path.insert(0, str(Path("../_ext").absolute()))
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-project = "py-crane"
-copyright = "2025, DNV AS. All rights reserved."
-author = "Siegfried Eisinger, Jorge Luis Mendez"
+project = "crane-controller"
+copyright = "2026, DNV AS. All rights reserved."
+author = "Siegfried Eisinger, Aleksandar Babic"
# The full version, including alpha/beta/rc tags
-release = "0.3.0"
+release = "0.0.1"
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
@@ -40,18 +39,8 @@
"sphinx.ext.autosummary",
"sphinx.ext.todo",
"sphinxcontrib.mermaid",
- "get_from_code",
- "spec",
- "sphinx.ext.graphviz",
- "sphinx.ext.inheritance_diagram",
- "sphinx.ext.intersphinx",
- # "sphinx.ext.autosectionlabel",
]
-intersphinx_mapping = {
- # "component-model": ("https://github.com/dnv-opensource/component-model/", None),
-}
-
# Extenstion for myst_parser
myst_enable_extensions = [
"dollarmath",
@@ -76,10 +65,10 @@
# -- Options for HTML output -------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
-html_title = f"py-crane {release}"
+html_title = f"crane-controller {release}"
html_theme = "furo"
html_static_path = ["_static"]
-# html_logo = "_static/py_crane.svg"
+html_logo = "_static/crane-controller.svg"
autodoc_default_options = {
"member-order": "groupwise",
"undoc-members": True,
@@ -89,8 +78,7 @@
myst_heading_anchors = 3
-todo_include_todos = True
-spec_include_specs = True
+todo_include_todos = False
# add markdown mermaid support
myst_fence_as_directive = ["mermaid"]
diff --git a/docs/source/crane_controller.rst b/docs/source/crane_controller.rst
index f78f7a0..0562a4e 100644
--- a/docs/source/crane_controller.rst
+++ b/docs/source/crane_controller.rst
@@ -1,15 +1,16 @@
-py-crane package
-==================
+crane-controller package
+========================
-.. Subpackages
-.. -----------
+Subpackages
+-----------
-.. .. autosummary::
-.. :toctree: _autosummary
-.. :template: custom-module.rst
-.. :recursive:
+.. autosummary::
+ :toctree: _autosummary
+ :template: custom-module.rst
+ :recursive:
-.. py_crane.utils
+ crane_controller.envs
+ crane_controller.wrappers
Modules
@@ -20,10 +21,8 @@ Modules
:template: custom-module.rst
:recursive:
- py_crane.crane
- py_crane.crane_fmu
- py_crane.boom
- py_crane.boom_fmu
- py_crane.mobile_crane
- py_crane.enum
- py_crane.animation
+ crane_controller.algorithm
+ crane_controller.crane_factory
+ crane_controller.ppo_agent
+ crane_controller.q_agent
+ crane_controller.reinforce_agent
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 18a6586..4b9f634 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,13 +1,14 @@
-.. py-crane documentation master file
+.. crane-controller documentation master file
-py-crane Documentation
-=======================
+crane-controller Documentation
+==============================
.. toctree::
:maxdepth: 4
:caption: Contents:
README
+ assurance
api
CHANGELOG
STYLEGUIDE
diff --git a/names.csv b/names.csv
new file mode 100644
index 0000000..883b96a
--- /dev/null
+++ b/names.csv
@@ -0,0 +1,30 @@
+Variable,Value
+PackageNameDashed,crane-controller
+PackageNameCapitalized,CraneController
+GitHubOrganisation,dnv-opensource
+SubPackage1Name,envs
+Module1Name,controlled_crane_pendulum
+SubPackage2Name,wrappers
+Module2Name,clip_reward
+Author1Name,Siegfried Eisinger
+Author1Email,siegfried.eisinger@dnv.com
+Author1LinkedIn,https://www.linkedin.com/in/siegfried-eisinger-a337638b
+Author2Name,Aleksandar Babic
+Author2Email,aleksandar.babic@dnv.com
+Author2LinkedIn,https://www.linkedin.com/in/aleksandar-babic-no
+Author3Name,
+Author3Email,
+Author3LinkedIn,
+Maintainer1Name,Claas Rostock
+Maintainer1Email,claas.rostock@dnv.com
+Maintainer1LinkedIn,https://www.linkedin.com/in/claasrostock/?locale=en_US
+Maintainer2Name,
+Maintainer2Email,
+Maintainer2LinkedIn,
+Keyword1,simulation
+Keyword2,model
+Keyword3,FMI
+Keyword4,OSP
+Keyword5,control algorithm
+Keyword6,crane
+Keyword7,anti-pendulum
diff --git a/plans/.plan.md b/plans/.plan.md
new file mode 100644
index 0000000..44baa2a
--- /dev/null
+++ b/plans/.plan.md
@@ -0,0 +1,39 @@
+# Plan:
+
+## Problem Statement
+
+
+## Baseline
+-
+-
+
+## Context
+- Relevant modules:
+- Related tests:
+- Constraints (performance, compatibility, regulation, tooling):
+
+## Assumptions
+-
+
+## Proposed Approach
+1.
+2.
+3.
+
+## Alternatives Considered
+- Option A:
+- Option B:
+
+## Validation Strategy
+- Tests to add or update:
+- Edge cases to cover:
+- How success will be verified:
+
+## Risks & Mitigations
+- Risk:
+ - Mitigation:
+
+## Definition of Done
+- [ ] Code implemented
+- [ ] Tests passing
+- [ ] Documentation updated (if needed)
diff --git a/plans/add-missing-docstrings/plan-draft.md b/plans/add-missing-docstrings/plan-draft.md
new file mode 100644
index 0000000..6afedf7
--- /dev/null
+++ b/plans/add-missing-docstrings/plan-draft.md
@@ -0,0 +1,54 @@
+# Plan: Add missing docstrings
+
+## Problem Statement
+In the current code base, many public functions, methods, attributes, classes, modules and sub-packages are still missing docstrings.
+Goal is to ensure all public functions, methods, attributes, classes, modules and sub-packages have docstrings.
+
+## Baseline
+- Run `uv run ruff check src/ scripts/` and record the current error count (should be 0 with D10x rules ignored).
+- Run `uv run ruff check src/ scripts/ --select D100,D101,D102,D103,D104,D105,D107` and record the violation count per rule.
+- Run `uv run ruff format --check` and confirm formatting is clean.
+- Run `uv run pytest` and record how many tests pass.
+- Inventory all missing docstrings by file, listing the symbol name and violation rule.
+
+## Proposed Approach
+Use `uv run ruff check` to identify missing docstrings and monitor your progress.
+In ruff.toml, following ruff rules from the `D10x` family are currently deactivated, i.e. listed as "temporarily ignored":
+ "D100", # Missing docstring in public module
+ "D101", # Missing docstring in public class
+ "D102", # Missing docstring in public method
+ "D103", # Missing docstring in public function
+ "D104", # Missing docstring in public package
+ "D105", # Missing docstring in magic method
+ "D107", # Missing docstring in __init__
+Goal is to eventually have these rules reactivated again (i.e. commented out in ruff.toml).
+As you progress, consider to sequentially re-activate the rules in ruff.toml, by step-by-step commenting them back out, then add the respective docstrings where flagged missing until `uv run ruff check` reports no more missing docstrings, and then repeat same for the next `D10x` rule.
+Work in phases, "inside-out": Start with doctsrings for symbols defined in a smaller / more local scope to docstrings for symbols defined in a more global scope.
+In each phase:
+- Create docstrings where missing.
+- Ensure any new docstrings are numpy-style compliant.
+- Add the `Parameters` section and ensure it is complete.
+- Add the `Returns` section in case the documented function or method returns anything different from `None`.
+- Add the `Raises` section in case the documented function or method raises exceptions.
+- If you feel it is reasonable and adds clarity, you can optionally add further numpydoc-defined sections (`Warns`, `See also`, `Notes`, `References`, `Examples`).
+
+## Context
+- Relevant modules: all (`src/**`, `scripts/**`)
+- Related tests: None
+- Constraints:
+ - Do **not** use ambiguous characters in docstrings, like for example — (em-dash) and – (en-dash). Use unambiguous ASCII characters instead, e.g. - (hyphen). Exemptions are characters which are explicitely listed in `allowed-confusables` in ruff.toml
+ - Run `uv run ruff format` after every phase
+ - Run `uv run ruff check` to monitor progress and ensure there is no regression in other ruff checks.
+
+## Validation Strategy
+- Run `uv run ruff check` to ensure there is no regression in ruff checks.
+- Run `uv run ruff format --check` to ensure formatting is not broken.
+- Run `uv run pytest` to ensure there is no regression in tests.
+
+## Definition of Done
+- [ ] In ruff.toml, all temporarily ignored `D10x` rules are activated again, i.e. commented out again.
+- [ ] All missing docstrings are added.
+- [ ] `uv run ruff format --check`
+- [ ] `uv run ruff check` passes
+- [ ] `uv run pytest` — all tests pass
+- [ ] Documentation updated (if needed)
diff --git a/plans/add-type-hints/plan-draft.md b/plans/add-type-hints/plan-draft.md
new file mode 100644
index 0000000..137b0d0
--- /dev/null
+++ b/plans/add-type-hints/plan-draft.md
@@ -0,0 +1,44 @@
+# Plan: Add type annotations
+
+## Problem Statement
+The code base in this project is not fully typed.
+The goal is to add type annotations where missing to:
+* public classes
+* arguments and return types of public functions, methods, and properties
+* public attributes and variables
+* private classes
+* arguments and return types of private functions, methods, and properties
+* private attributes and variables
+
+## Baseline
+- Run `uv run ruff check src/ scripts/ tests/ --select ANN` and record the current violation count per rule.
+- Run `uv run ruff check src/ scripts/ tests/` and record the current error count.
+- Run `uv run ruff format --check` and confirm formatting is clean.
+- Run `uv run pytest` and record how many tests pass.
+
+## Approach
+
+Work in phases, ordered from easiest-to-resolve types to hardest-to-resolve types.
+
+## Context
+
+- Relevant modules: all (`src/**`, `tests/**`, `scripts/**`)
+- Related tests: all (run `uv run pytest` to guard against regressions)
+- Constraints:
+ - Run `uv run ruff format` after every phase
+ - Existing tests must continue to pass after every phase
+
+## Validation Strategy
+
+- Run `uv run ruff --select ANN`, first to yield the baseline, and then after each phase as a monitoring metric to validate that number of missing type annotations is decreasing.
+- Run `uv run ruff format --check` to ensure formatting is not broken.
+- Run `uv run ruff check` to ensure no regressions in ruff checks.
+
+## Definition of Done
+
+- [ ] Type annotations added (all phases complete)
+- [ ] `uv run ruff --select ANN` reports fewer than 5 missing type annotations (target: 0)
+- [ ] `uv run ruff format --check` — no format violations
+- [ ] `uv run ruff check` — no regression in ruff checks
+- [ ] `uv run pytest` — all tests pass
+- [ ] No rules added to the `ignore` lists in `ruff.toml` and `pyproject.toml` without documented justification
diff --git a/plans/add-type-stubs/plan-draft.md b/plans/add-type-stubs/plan-draft.md
new file mode 100644
index 0000000..1596fba
--- /dev/null
+++ b/plans/add-type-stubs/plan-draft.md
@@ -0,0 +1,45 @@
+# Plan: Add type stubs for 3rd party packages
+
+## Problem Statement
+The code in this project uses multiple third party packages.
+In order to improve typing in our code base, type-stibs for the most relevant third party package(s) shall be created.
+The goal is to have type stubs for those modules and symbols from third party packages which are used in the current code base.
+
+## Baseline
+- Run `uv run pyright` and record the current error/warning/information counts.
+- Run `uv run mypy` and record the current error count.
+- Identify which third-party imports lack type stubs (look for `reportMissingTypeStubs` in pyright output and `import-untyped` in mypy output).
+- Run `uv run ruff check src/ scripts/ tests/` and record the current error count.
+- Run `uv run pytest` and record how many tests pass.
+
+## Approach
+
+* First, determine which (untyped) third party packages have relevant (negative) impact on typing in our code base. In other words: Which third party packages should we concentrate on to create type-stubs.
+* Work in phases, ordered from easiest-to-infer types to hardest-to-infer types.
+* A good example of how a type-stub structure for a third party package can look like already exists in this repo's own `stubs/` directory. Study the existing stub packages there as a model for style, depth, and registration in `pyproject.toml` / `pyrightconfig.json`. Follow a similar approach when creating type-stubs for additional packages.
+* For each 3rd-party class you stub, trace all attribute access chains used in the codebase (e.g. `.venv.envs[0]`). Stub every intermediate class and attribute that appears in such a chain, not only the directly imported symbols.
+
+## Context
+
+- Relevant modules: all (`src/**`, `tests/**`, `scripts/**`)
+- Related tests: all (run `uv run pytest` to guard against regressions)
+- Constraints:
+ - Run `uv run ruff format` after every phase
+ - Existing tests must continue to pass after every phase
+ - **`py.typed` check:** Before creating stubs for a package, check whether it ships a `py.typed` marker. If it does, mypy will use the package's own inline types and ignore custom stubs in `stubs/`. Stubs in that case only benefit pyright (which prefers local stubs). Factor this into prioritization.
+
+## Validation Strategy
+
+- Run `uv run ruff format --check` to ensure formatting is not broken.
+- Run `uv run ruff check` to ensure no regressions in ruff checks.
+- Run `uv run ruff --select ANN`, `uv run pyright`, and `uv run mypy` to monitor, and validate that number of missing type hints in the code is decreasing.
+- After adding stubs, scan all source files for `# type: ignore` comments that have become unnecessary. Use pyright's `reportUnnecessaryTypeIgnoreComment` (surfaces as "information" level) and `uv run mypy --warn-unused-ignores` to detect them. Remove stale suppressions.
+
+## Definition of Done
+
+- [ ] Type-stubs added for the most relevant third party package (all phases complete)
+- [ ] `uv run ruff --select ANN`, `uv run pyright`, and `uv run mypy` report fewer than 5 missing type annotations (target: 0)
+- [ ] `uv run ruff format --check` — no format violations
+- [ ] `uv run ruff check` — no regression in ruff checks
+- [ ] `uv run pytest` — all tests pass
+- [ ] No rules added to the `ignore` lists in `ruff.toml` and `pyproject.toml` without documented justification
diff --git a/plans/fix-mypy/plan-draft.md b/plans/fix-mypy/plan-draft.md
new file mode 100644
index 0000000..3a3a968
--- /dev/null
+++ b/plans/fix-mypy/plan-draft.md
@@ -0,0 +1,49 @@
+# Plan: Fix issues raised by mypy
+
+## Problem Statement
+
+`mypy` currently raises various errors across the `src/` tree.
+The goal is to reduce the error count to **below 5** (ideally zero) and eliminate as many warnings as practical by fixing the underlying code — not by suppressing rules.
+
+## Baseline
+- Run `uv run mypy` and record the current error count.
+- Run `uv run mypy --warn-unused-ignores` and record stale `# type: ignore` comments. These often represent the bulk of the work.
+- Categorize errors by error code (e.g. `arg-type`, `assignment`, `override`, `attr-defined`).
+- Run `uv run ruff check src/ scripts/ tests/` and record the current error count.
+- Run `uv run ruff format --check` and confirm formatting is clean.
+- Run `uv run pytest` and record how many tests pass.
+
+## Approach
+
+Work in phases, ordered from easiest mechanical fixes to the most structural refactors.
+After each phase: run `uv run ruff format`, `uv run mypy`, and `uv run pytest`.
+
+### pyright / mypy divergence on `# type: ignore`
+
+`# type: ignore` suppresses diagnostics for **both** mypy and pyright. `# pyright: ignore` suppresses only pyright. When mypy no longer needs a `# type: ignore` but pyright still does, replace it with `# pyright: ignore[rule]`. Always run both `uv run mypy --warn-unused-ignores` and `uv run pyright` after each change to avoid breaking one tool while fixing the other.
+
+## Context
+
+- Relevant modules: all (`src/**`, `tests/**`, `scripts/**`, `stubs/**`)
+- Related tests: all (run `uv run pytest` to guard against regressions)
+- Constraints:
+ - Do **not** suppress rules globally in `pyproject.toml`. The goal is to actually fix the code, not hide errors.
+ - Suppress per-line with `# type: ignore` only as a last resort for genuinely inapplicable rules.
+ - Run `uv run ruff format` after every phase
+ - Existing tests must continue to pass after every phase
+
+## Validation Strategy
+
+- Run `uv run mypy` and confirm error count is below 5 (ideally zero).
+- Run `uv run mypy --warn-unused-ignores` to detect `# type: ignore` comments that have become unnecessary (e.g. after fixing stubs or annotations). Remove stale suppressions.
+- Run `uv run pytest` and confirm all tests pass.
+- Run `uv run ruff format --check` to ensure formatting is not broken.
+
+## Definition of Done
+
+- [ ] Code implemented (all phases complete)
+- [ ] `uv run mypy` reports fewer than 5 errors (target: 0)
+- [ ] `uv run mypy --warn-unused-ignores` reports no unused ignore comments
+- [ ] `uv run pytest` — all tests pass
+- [ ] `uv run ruff format --check` — no format violations
+- [ ] No rules added to the `ignore` list in `pyproject.toml` without documented justification
diff --git a/plans/fix-pyright/plan-draft.md b/plans/fix-pyright/plan-draft.md
new file mode 100644
index 0000000..a100c25
--- /dev/null
+++ b/plans/fix-pyright/plan-draft.md
@@ -0,0 +1,50 @@
+# Plan: Fix issues raised by pyright
+
+## Problem Statement
+
+`pyright` currently raises various errors across the `src/` tree.
+The goal is to reduce the error count to **below 5** (ideally zero) and eliminate as many warnings as practical by fixing the underlying code — not by suppressing rules.
+
+## Baseline
+- Run `uv run pyright` and record the current error, warning, and information counts.
+- Categorize errors by type (e.g. `reportGeneralClassIssues`, `reportAttributeAccessIssue`, `reportArgumentType`).
+- Run `uv run ruff check src/ scripts/ tests/` and record the current error count.
+- Run `uv run ruff format --check` and confirm formatting is clean.
+- Run `uv run pytest` and record how many tests pass.
+
+## Approach
+
+Work in phases, ordered from easiest mechanical fixes to the most structural refactors.
+After each phase: run `uv run ruff format`, `uv run pyright`, and `uv run pytest`.
+
+When you see many `reportUnknownMemberType` warnings cascading from a single variable, the root cause is usually that the variable itself is typed as `Unknown` (often due to an incomplete stub or an untyped return value). Fix it by adding an explicit type annotation at the assignment point rather than chasing each usage individually.
+
+Include a cleanup phase after fixing each category of pyright errors: re-run pyright and remove any `# type: ignore` or `# pyright: ignore` comments that are flagged as `reportUnnecessaryTypeIgnoreComment`.
+
+### pyright / mypy divergence on `# type: ignore`
+
+When removing or modifying `# type: ignore` comments, check that mypy still passes (`uv run mypy`). If mypy still needs the suppression but pyright does not, keep the `# type: ignore` with only the mypy-needed error codes. If pyright needs it but mypy does not, use `# pyright: ignore[rule]` instead.
+
+## Context
+
+- Relevant modules: all (`src/**`, `tests/**`, `scripts/**`, `stubs/**`)
+- Related tests: all (run `uv run pytest` to guard against regressions)
+- Constraints:
+ - Do **not** suppress rules globally in `pyproject.toml`. The goal is to actually fix the code, not hide errors.
+ - Suppress per-line with `# pyright: ignore` only as a last resort for genuinely inapplicable rules.
+ - Run `uv run ruff format` after every phase
+ - Existing tests must continue to pass after every phase
+
+## Validation Strategy
+
+- Run `uv run pyright` and confirm error count is below 5 (ideally zero).
+- Run `uv run pytest` and confirm all tests pass.
+- Run `uv run ruff format --check` to ensure formatting is not broken.
+
+## Definition of Done
+
+- [ ] Code implemented (all phases complete)
+- [ ] `uv run pyright` reports 0 errors and no `reportUnnecessaryTypeIgnoreComment` informations
+- [ ] `uv run pytest` — all tests pass
+- [ ] `uv run ruff format --check` — no format violations
+- [ ] No rules added to the `ignore` list in `pyproject.toml` without documented justification
diff --git a/plans/fix-ruff-check/plan-draft.md b/plans/fix-ruff-check/plan-draft.md
new file mode 100644
index 0000000..b003213
--- /dev/null
+++ b/plans/fix-ruff-check/plan-draft.md
@@ -0,0 +1,41 @@
+# Plan: Fix issues raised by ruff check
+
+## Problem Statement
+
+`ruff check` currently raises various errors across the `src/` tree.
+The goal is to reduce the error count to **below 10** (ideally zero) by systematically fixing each violation category — not by suppressing rules.
+
+## Baseline
+- Run `uv run ruff check src/ scripts/` and record the current error count and violation categories.
+- Run `uv run ruff check src/ scripts/ --statistics` to get a per-rule summary.
+- Run `uv run ruff format --check` and confirm formatting is clean.
+- Run `uv run pytest` and record how many tests pass.
+
+## Approach
+
+Work in phases, ordered from easiest mechanical fixes to the most structural refactors.
+After each phase: run `uv run ruff format`, `uv run ruff check`, and `uv run pytest`.
+
+## Context
+
+- Relevant modules: all (`src/**`, `tests/**`, `stubs/**`)
+- Related tests: all (run `uv run pytest` to guard against regressions)
+- Constraints:
+ - Do **not** suppress rules globally in `ruff.toml`. The goal is to actually fix the code, not hide errors.
+ - Suppress per-line with `# noqa` only as a last resort for cases where the lint rule is genuinely inapplicable.
+ - Run `uv run ruff format` after every phase
+ - Existing tests must continue to pass after every phase
+
+## Validation Strategy
+
+- Run `uv run ruff check` and confirm error count is below 10 (ideally zero).
+- Run `uv run pytest` and confirm all tests pass.
+- Run `uv run ruff format --check` to ensure formatting is not broken.
+
+## Definition of Done
+
+- [ ] Code implemented (all phases complete)
+- [ ] `uv run ruff check` reports fewer than 10 errors (target: 0)
+- [ ] `uv run pytest` — all tests pass
+- [ ] `uv run ruff format --check` — no format violations
+- [ ] No rules added to the `ignore` list in `ruff.toml` without documented justification
diff --git a/plans/reformat-existing-docstrings/plan-draft.md b/plans/reformat-existing-docstrings/plan-draft.md
new file mode 100644
index 0000000..38e7497
--- /dev/null
+++ b/plans/reformat-existing-docstrings/plan-draft.md
@@ -0,0 +1,40 @@
+# Plan: Reformat existing docstrings
+
+## Problem Statement
+In the current code base, existing docstrings are formatted in various, non-uniform styles.
+Goal is to reformat all existing docstrings to be numpy-style compliant.
+
+## Baseline
+- Run `uv run ruff check src/ scripts/` and record the current error count (should be 0 with D10x rules ignored).
+- Run `uv run ruff check src/ scripts/ --select D` to identify all existing docstring-style violations.
+- Run `uv run ruff format --check` and confirm formatting is clean.
+- Run `uv run pytest` and record how many tests pass.
+- Inventory all files with existing docstrings and their count per file.
+
+## Proposed Approach
+- Reformat existing docstrings to be numpy-style compliant.
+- Ensure the `Parameters` section exists and is complete.
+- Ensure that `Returns` section exists and is complete in case the documented function or method returns anything different from `None`.
+- Ensure that `Raises` section exists and is complete in case the documented function or method raises exceptions.
+- If you feel it is reasonable and adds clarity, you can optionally add further numpydoc-defined sections (`Warns`, `See also`, `Notes`, `References`, `Examples`).
+- Eventually, improve the wording of docstrings where you feel the existing description is incorrect or outdated.
+
+## Context
+- Relevant modules: all (`src/**`, `scripts/**`)
+- Related tests: None
+- Constraints:
+ - Do **not** use ambiguous characters in docstrings (em-dash `—`, en-dash `–`, etc.). Use ASCII equivalents (e.g. hyphen `-`). Exemptions: characters listed in `allowed-confusables` in `ruff.toml`.
+ - Run `uv run ruff format` after every phase
+ - Run `uv run ruff check` to monitor progress and ensure there is no regression in other ruff checks.
+
+## Validation Strategy
+- Run `uv run ruff check` to ensure there is no regression in ruff checks.
+- Run `uv run ruff format --check` to ensure formatting is not broken.
+- Run `uv run pytest` to ensure there is no regression in tests.
+
+## Definition of Done
+- [ ] All existing docstrings reformated to be numpy-style compliant.
+- [ ] `uv run ruff format --check`
+- [ ] `uv run ruff check` passes
+- [ ] `uv run pytest` — all tests pass
+- [ ] Documentation updated (if needed)
diff --git a/pyproject.toml b/pyproject.toml
index 8392b8a..5c17c68 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,27 +1,46 @@
[build-system]
- requires = ["hatchling"]
- build-backend = "hatchling.build"
+requires = [
+ "hatchling",
+]
+build-backend = "hatchling.build"
-[tool.hatch.build]
-exclude = ["pkg_resources"]
+[tool.hatch.build.targets.sdist]
+only-include = [
+ "src/crane_controller",
+ "scripts",
+ "tests",
+ ".coveragerc",
+ ".editorconfig",
+ ".pre-commit-config.yaml",
+ "pyproject.toml",
+ "pytest.ini",
+ "ruff.toml",
+ "uv.lock",
+]
[tool.hatch.build.targets.wheel]
- packages = ["src/crane_controller"]
+packages = [
+ "src/crane_controller",
+]
[tool.hatch.metadata]
- allow-direct-references = true
+allow-direct-references = true
[project]
- name = "crane_controller"
- version = "0.0.1"
- description = "AI-based controller of the crane."
- readme = "README.rst"
- requires-python = ">= 3.11"
- license = { file = "LICENSE" }
- authors = [
- { name = "Siegfried Eisinger", email = "Siegfried.Eisinger@dnv.com" },
- { name = "Henrik Stokland Berg", enail = "Henrik.Stokland.Berg@dnv.com" },
- { name = "Sebastian.Winter", email = "Sebastian.Winter@dnv.com" },
+name = "crane-controller"
+version = "0.0.1"
+description = "AI-based control of a crane system using reinforcement learning."
+readme = "README.rst"
+requires-python = ">= 3.11"
+license = { file = "LICENSE" }
+authors = [
+ { name = "Siegfried Eisinger", email = "siegfried.eisinger@dnv.com" },
+ { name = "Aleksandar Babic", email = "aleksandar.babic@dnv.com" },
+ { name = "Henrik Stokland Berg", email = "henrik.stokland.berg@dnv.com" },
+ { name = "Sebastian Winter", email = "sebastian.winter@dnv.com" },
+]
+maintainers = [
+ { name = "Claas Rostock", email = "claas.rostock@dnv.com" },
]
keywords = [
"simulation",
@@ -29,39 +48,94 @@ keywords = [
"FMI",
"OSP",
"control algorithm",
+ "crane",
+ "anti-pendulum",
+]
+classifiers = [
+ "Development Status :: 3 - Alpha",
+ "License :: OSI Approved :: MIT License",
+ "Programming Language :: Python :: 3.11",
+ "Programming Language :: Python :: 3.12",
+ "Programming Language :: Python :: 3.13",
+ "Operating System :: Microsoft :: Windows",
+ "Operating System :: POSIX :: Linux",
+ # "Operating System :: MacOS",
+ "Environment :: Console",
+ "Intended Audience :: Developers",
+ "Intended Audience :: Science/Research",
+ "Topic :: Scientific/Engineering",
+ "Topic :: Software Development :: Libraries :: Python Modules",
]
dependencies = [
- "py-crane",
- "gymnasium",
- "matplotlib>=3.10.7",
- "numpy>=2.0",
- "scipy>=1.15.1",
- "pygame>=2.1.3",
- "pre-commit",
- "tqdm>=4.67.3",
- "pandas>=3.0.0",
- "seaborn>=0.13.2",
- "torch>=2.10.0",
- "stable-baselines3[extra]>=2.7.1",
+ "py-crane>=0.3.1",
+ "numpy>=2.4",
+ "scipy>=1.17.1",
+ "pandas>=3.0",
+ "torch==2.10.0",
+ "gymnasium>=1.2.3",
+ "pygame-ce>=2.5", # replacement for pygame (transitive dependency of gymnasium)
+ "stable-baselines3[extra]>=2.8", # does not support Python 3.14 (By the time of writing, 2026-04-22)
+ "matplotlib>=3.10",
+ "seaborn>=0.13.2",
+ "tqdm>=4.67",
+]
+
+[project.optional-dependencies]
+cuda = [
+ "torch==2.10.0+cu130",
+]
+
+[project.urls]
+Homepage = "https://github.com/dnv-opensource/crane-controller"
+Documentation = "https://dnv-opensource.github.io/crane-controller/README.html"
+Repository = "https://github.com/dnv-opensource/crane-controller.git"
+Issues = "https://github.com/dnv-opensource/crane-controller/issues"
+Changelog = "https://github.com/dnv-opensource/crane-controller/blob/main/CHANGELOG.md"
+
+
+[dependency-groups]
+dev = [
+ "pytest>=9.0",
+ "pytest-cov>=7.1",
+ "ruff>=0.15.9",
+ "pyright>=1.1.408",
+ "mypy>=1.19.1",
+ "sourcery>=1.43.0",
+ "pre-commit>=4.5",
+ "Sphinx>=9.0",
+ "sphinx-argparse-cli>=1.21.3",
+ "sphinx-autodoc-typehints>=3.6",
+ "sphinxcontrib-mermaid>=2.0",
+ "myst-parser>=5.0",
+ "furo>=2025.12",
+ "jupyter>=1.1.1",
+ "nbconvert>=7.17",
+ "pandas-stubs>=3.0",
+ "scipy-stubs>=1.17.1",
]
[tool.uv]
-dev-dependencies = [
- "pytest>=8.3",
- "pytest-cov>=5.0",
- "ruff>=0.6.3",
- "pyright>=1.1.378",
- "mypy>=1.11.1",
- "sourcery>=1.22",
- "pre-commit>=3.8",
- "Sphinx>=8.0",
- "sphinx-argparse-cli>=1.17",
- "sphinx-autodoc-typehints>=2.2",
- "sphinxcontrib-mermaid>=1.0.0",
- "myst-parser>=4.0",
- "furo>=2024.8",
+default-groups = [
+ "dev",
+]
+find-links = [
+ "https://download.pytorch.org/whl/torch",
]
native-tls = true
+required-environments = [
+ "sys_platform == 'win32'",
+ "sys_platform == 'linux'",
+ # "sys_platform == 'darwin'",
+]
+override-dependencies = [
+ # Problem: gymnasium requires pygame which does not have wheels for Python 3.14+
+ # Solution: Force pygame-ce which is a compatible fork of pygame and has wheels for Python 3.14+.
+ # Practical solution, though, is a bit of a hack and needs two parts:
+ # 1) Add pygame-ce as dependency (as replacement for pygame).
+ # 2) Remove pygame via override-dependencies here:
+ "pygame; python_version < '0'", # this will never be selected, effectively removing pygame
+]
+
[tool.mypy]
plugins = []
@@ -79,6 +153,7 @@ disable_error_code = [
"import-untyped",
]
+
[tool.pyright]
stubPath = "stubs"
include = [
@@ -86,5 +161,44 @@ include = [
"tests",
]
exclude = [
- "tests/test_working_directory",
+ "**/node_modules",
+ "**/__pycache__",
+ "**/.*",
+ ".venv",
+ "src/folder_to_be_excluded",
]
+
+typeCheckingMode = "basic"
+useLibraryCodeForTypes = true
+reportMissingParameterType = "error"
+reportUnknownParameterType = "warning"
+reportUnknownMemberType = "warning" # consider to set to `false` if you work a lot with matplotlib and pandas, which are both not properly typed and known to trigger this warning
+reportMissingTypeArgument = "error"
+reportPropertyTypeMismatch = "error"
+reportFunctionMemberAccess = "warning"
+reportPrivateUsage = "warning"
+reportTypeCommentUsage = "warning"
+reportIncompatibleMethodOverride = "warning"
+reportIncompatibleVariableOverride = "error"
+reportInconsistentConstructor = "error"
+reportOverlappingOverload = "warning"
+reportUninitializedInstanceVariable = "warning"
+reportCallInDefaultInitializer = "warning"
+reportUnnecessaryIsInstance = "information"
+reportUnnecessaryCast = "warning"
+reportUnnecessaryComparison = "warning"
+reportUnnecessaryContains = "warning"
+reportUnusedCallResult = "warning"
+reportUnusedExpression = "warning"
+reportMatchNotExhaustive = "warning"
+reportUntypedFunctionDecorator = "warning"
+reportUntypedClassDecorator = "warning"
+reportUntypedBaseClass = "error"
+reportUntypedNamedTuple = "warning"
+reportUnnecessaryTypeIgnoreComment = "information"
+# Activate the following rules only locally and temporary, i.e. for a QA session.
+# (For server side CI they are considered too strict.)
+# reportMissingTypeStubs = true
+# reportConstantRedefinition = "warning"
+# reportImportCycles = "warning"
+# reportImplicitStringConcatenation = "warning"
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000..7eb9275
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,7 @@
+[pytest]
+testpaths =
+ tests
+addopts = --strict-markers --verbose --durations=10
+xfail_strict = True
+markers =
+ slow: marks tests as slow (deselect with '-m "not slow"')
diff --git a/ruff.toml b/ruff.toml
index 7e6788d..1ffd546 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -17,34 +17,35 @@ target-version = "py311"
# Start by including _all_ Ruff lint rules, then ignore selected rules as needed
# https://docs.astral.sh/ruff/rules/
select = [
- "E",
- "D",
- "F",
- "N",
- "W",
- "I",
- "B",
+ "ALL",
+ # "E",
+ # "D",
+ # "F",
+ # "N",
+ # "W",
+ # "I",
+ # "B",
]
ignore = [
- # Ruff lint rules temporarily ignored to match the ruff configuration used so far in this repo
- "E501", # Line length too long
- "D100", # Missing docstring in public module
- "D101", # Missing docstring in public class
- "D102", # Missing docstring in public method
- "D103", # Missing docstring in public function
- "D104", # Missing docstring in public package
- "D105", # Missing docstring in magic method
- "D107", # Missing docstring in __init__
- "D202", # No blank lines allowed after function docstring
- "D203", # 1 blank line required before class docstring
- "D205", # 1 blank line required between summary line and description
- "D212", # Multi-line docstring summary should start at the first line
- "D213", # Multi-line docstring summary should start at the second line
- # "N802", # Function name should be lowercase (uncomment if you want to allow Uppercase function names)
- "N803", # Argument name should be lowercase (uncommented. Lower-camelCase is accepted for variables related to XML and FMI standard)
- "N806", # Variable in function should be lowercase (uncomment if you want to allow Uppercase variable names in functions)
- # "N815", # Variable in class scope should not be mixedCase (uncomment if you want to allow mixedCase variable names in class scope)
- # "N816", # Variable in global scope should not be mixedCase (uncomment if you want to allow mixedCase variable names in global scope)
+ # Ruff lint rules temporarily ignored, but which should be reactivated and resolved in the future.
+ # "E501", # Line length too long
+ # "D100", # Missing docstring in public module
+ # "D101", # Missing docstring in public class
+ # "D102", # Missing docstring in public method
+ # "D103", # Missing docstring in public function
+ # "D104", # Missing docstring in public package
+ # "D105", # Missing docstring in magic method
+ # "D107", # Missing docstring in __init__
+ # "D202", # No blank lines allowed after function docstring
+ # "D203", # 1 blank line required before class docstring
+ # "D205", # 1 blank line required between summary line and description
+ # "D212", # Multi-line docstring summary should start at the first line
+ # "D213", # Multi-line docstring summary should start at the second line
+ # # "N802", # Function name should be lowercase (uncomment if you want to allow Uppercase function names)
+ # "N803", # Argument name should be lowercase (uncommented. Lower-camelCase is accepted for variables related to XML and FMI standard)
+ # "N806", # Variable in function should be lowercase (uncomment if you want to allow Uppercase variable names in functions)
+ # # "N815", # Variable in class scope should not be mixedCase (uncomment if you want to allow mixedCase variable names in class scope)
+ # # "N816", # Variable in global scope should not be mixedCase (uncomment if you want to allow mixedCase variable names in global scope)
# Ruff lint rules considered as too strict and hence ignored
"FIX002", # Line contains TODO, consider resolving the issue
@@ -179,10 +180,14 @@ raises-require-match-for = [
"A001", # Variable {name} is shadowing a Python builtin
"D100", # Missing docstring in public module
]
-
+# types.py specific ignores
+"**/types.py" = [
+ "A005", # Module `types` shadows a Python standard-library module
+]
[lint.pydocstyle]
convention = "numpy"
[format]
docstring-code-format = true
+# preview = true # uncomment to enable preview features of Ruff formatter (https://docs.astral.sh/ruff/formatter/#preview-features)
diff --git a/scripts/__init__.py b/scripts/__init__.py
new file mode 100644
index 0000000..0b216e1
--- /dev/null
+++ b/scripts/__init__.py
@@ -0,0 +1 @@
+"""Command-line helper scripts for training, playback, and analysis."""
diff --git a/scripts/analyse_q.py b/scripts/analyse_q.py
index 6d60815..56dd6b9 100644
--- a/scripts/analyse_q.py
+++ b/scripts/analyse_q.py
@@ -1,18 +1,23 @@
"""Inspect a trained Q-table for the AntiPendulumEnv.
-Prints a per-pos/speed average summary by default. Use --obs to drill
+Prints a per-pos/speed average summary by default. Use ``--obs`` to drill
into specific states; negative values act as wildcards (match any).
-The observation tuple has 5 dimensions:
- [energy, pos, speed, distance, sector]
+The observation tuple has 5 dimensions::
+
+ [energy, pos, speed, distance, sector]
+
+Examples
+--------
+.. code-block:: bash
-Example:
uv run python scripts/analyse_q.py --model-path tests/anti-pendulum.json
uv run python scripts/analyse_q.py --model-path tests/anti-pendulum.json --obs -1 0 0 -1 -1
uv run python scripts/analyse_q.py --model-path tests/anti-pendulum.json --obs -1 1 1 -1 -1
"""
import argparse
+import logging
import numpy as np
@@ -20,16 +25,25 @@
from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
from crane_controller.q_agent import QLearningAgent
+LOGGER = logging.getLogger(__name__)
+
-def _build_dummy_env():
- """Minimal env needed to satisfy QLearningAgent constructor (action_space.n)."""
+def _build_dummy_env() -> AntiPendulumEnv:
+ """Build a minimal env to satisfy the ``QLearningAgent`` constructor.
+
+ Returns
+ -------
+ AntiPendulumEnv
+ Environment with discrete observation space.
+ """
return AntiPendulumEnv(build_crane, discrete=QLearningAgent.DEFAULT_DISCRETE.copy())
-def main():
+def main() -> None:
+ """Parse CLI arguments and inspect a trained Q-table."""
parser = argparse.ArgumentParser(description="Inspect a trained Q-table for the crane anti-pendulum task.")
- parser.add_argument("--model-path", type=str, required=True, help="Path to a trained Q-table JSON")
- parser.add_argument(
+ _ = parser.add_argument("--model-path", type=str, required=True, help="Path to a trained Q-table JSON")
+ _ = parser.add_argument(
"--obs",
type=int,
nargs=5,
@@ -41,21 +55,23 @@ def main():
env = _build_dummy_env()
agent = QLearningAgent(env, trained=(args.model_path, True))
- print(f"Q-table: {len(agent.q_values)} states ({args.model_path})")
- print()
+ logging.basicConfig(level=logging.INFO, format="%(message)s")
+
+ LOGGER.info("Q-table: %s states (%s)", len(agent.q_values), args.model_path)
+ LOGGER.info("")
if args.obs:
- print(f"Q-values matching obs {args.obs} (columns: state, q-values, best-action, mean, cv)")
- print("-" * 72)
+ LOGGER.info("Q-values matching obs %s (columns: state, q-values, best-action, mean, cv)", args.obs)
+ LOGGER.info("%s", "-" * 72)
agent.analyse_q(tuple(args.obs))
else:
- print("Per-pos/speed average Q-values (actions: left=0, coast=1, right=2)")
- print("-" * 52)
+ LOGGER.info("Per-pos/speed average Q-values (actions: left=0, coast=1, right=2)")
+ LOGGER.info("%s", "-" * 52)
for pos in (0, 1):
for speed in (0, 1):
res = {k: v for k, v in agent.q_values.items() if k[1] == pos and k[2] == speed}
avgs = [np.average([x[i] for x in res.values()]) for i in range(3)]
- print(f" pos={pos} speed={speed} -> {[f'{a:.4f}' for a in avgs]}")
+ LOGGER.info(" pos=%s speed=%s -> %s", pos, speed, [f"{a:.4f}" for a in avgs])
if __name__ == "__main__":
diff --git a/scripts/play_ppo.py b/scripts/play_ppo.py
index 4060777..952859d 100644
--- a/scripts/play_ppo.py
+++ b/scripts/play_ppo.py
@@ -1,26 +1,33 @@
"""Run a trained PPO agent on the AntiPendulumEnv.
-Example:
+Examples
+--------
+.. code-block:: bash
+
uv run python scripts/play_ppo.py --model-path models/ppo_AntiPendulumEnv.zip
uv run python scripts/play_ppo.py --model-path models/ppo.zip --render-mode plot --episodes 3
"""
import argparse
+import logging
from crane_controller.crane_factory import build_crane
from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
from crane_controller.ppo_agent import ProximalPolicyOptimizationAgent
+LOGGER = logging.getLogger(__name__)
+
-def main():
+def main() -> None:
+ """Parse CLI arguments and run a trained PPO agent."""
parser = argparse.ArgumentParser(description="Run a trained PPO agent on the crane anti-pendulum task.")
- parser.add_argument("--model-path", type=str, required=True, help="Path to a trained .zip model")
- parser.add_argument("--render-mode", type=str, default="play-back", help="Render mode for playback")
- parser.add_argument("--episodes", type=int, default=1, help="Number of episodes to run")
+ _ = parser.add_argument("--model-path", type=str, required=True, help="Path to a trained .zip model")
+ _ = parser.add_argument("--render-mode", type=str, default="play-back", help="Render mode for playback")
+ _ = parser.add_argument("--episodes", type=int, default=1, help="Number of episodes to run")
args = parser.parse_args()
agent = ProximalPolicyOptimizationAgent(
- AntiPendulumEnv, # type: ignore[arg-type]
+ AntiPendulumEnv,
n_envs=0, # load-from-file mode
env_kwargs={
"crane": build_crane,
@@ -30,8 +37,9 @@ def main():
trained=(args.model_path, True),
)
+ logging.basicConfig(level=logging.INFO, format="%(message)s")
for episode in range(args.episodes):
- print(f"Episode {episode + 1}/{args.episodes}")
+ LOGGER.info("Episode %s/%s", episode + 1, args.episodes)
agent.do_one_episode(seed=episode + 1)
diff --git a/scripts/play_q.py b/scripts/play_q.py
index 6592d66..d24bb80 100644
--- a/scripts/play_q.py
+++ b/scripts/play_q.py
@@ -1,25 +1,32 @@
"""Run a trained Q-learning agent on the AntiPendulumEnv.
-Example:
+Examples
+--------
+.. code-block:: bash
+
uv run python scripts/play_q.py --model-path models/q_AntiPendulumEnv.json
uv run python scripts/play_q.py --model-path tests/anti-pendulum.json --render-mode plot --episodes 3
"""
import argparse
+import logging
from crane_controller.crane_factory import build_crane
from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
from crane_controller.q_agent import QLearningAgent
+LOGGER = logging.getLogger(__name__)
+
-def main():
+def main() -> None:
+ """Parse CLI arguments and run a trained Q-learning agent."""
parser = argparse.ArgumentParser(description="Run a trained Q-learning agent on the crane anti-pendulum task.")
- parser.add_argument("--model-path", type=str, required=True, help="Path to a trained Q-table JSON")
- parser.add_argument(
+ _ = parser.add_argument("--model-path", type=str, required=True, help="Path to a trained Q-table JSON")
+ _ = parser.add_argument(
"--render-mode", type=str, default="plot", help="Render mode (plot, play-back, reward-tracking)"
)
- parser.add_argument("--episodes", type=int, default=1, help="Number of episodes to run")
- parser.add_argument("--v0", type=float, default=-1.0, help="Initial crane speed (negative = stop mode)")
+ _ = parser.add_argument("--episodes", type=int, default=1, help="Number of episodes to run")
+ _ = parser.add_argument("--v0", type=float, default=-1.0, help="Initial crane speed (negative = stop mode)")
args = parser.parse_args()
env = AntiPendulumEnv(
@@ -30,9 +37,10 @@ def main():
)
agent = QLearningAgent(env, trained=(args.model_path, True))
+ logging.basicConfig(level=logging.INFO, format="%(message)s")
for episode in range(args.episodes):
- print(f"Episode {episode + 1}/{args.episodes}")
- env.reset(seed=episode + 1)
+ LOGGER.info("Episode %s/%s", episode + 1, args.episodes)
+ _ = env.reset(seed=episode + 1)
agent.do_episodes(n_episodes=1)
diff --git a/scripts/train_ppo.py b/scripts/train_ppo.py
index df0cbd4..2509037 100644
--- a/scripts/train_ppo.py
+++ b/scripts/train_ppo.py
@@ -1,40 +1,49 @@
"""Train a PPO agent on the AntiPendulumEnv.
-Example:
+Examples
+--------
+.. code-block:: bash
+
uv run python scripts/train_ppo.py
uv run python scripts/train_ppo.py --steps 500000 --n-envs 8 --save-path models/ppo.zip
uv run python scripts/train_ppo.py --dry-run
"""
import argparse
+import logging
from pathlib import Path
from crane_controller.crane_factory import build_crane
from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
from crane_controller.ppo_agent import ProximalPolicyOptimizationAgent
+LOGGER = logging.getLogger(__name__)
+
-def main():
+def main() -> None:
+ """Parse CLI arguments and train a PPO agent."""
parser = argparse.ArgumentParser(description="Train a PPO agent on the crane anti-pendulum task.")
- parser.add_argument("--steps", type=int, default=100_000, help="Total training timesteps")
- parser.add_argument("--n-envs", type=int, default=4, help="Number of parallel environments")
- parser.add_argument("--render-mode", type=str, default="none", help="Render mode during training")
- parser.add_argument(
+ _ = parser.add_argument("--steps", type=int, default=100_000, help="Total training timesteps")
+ _ = parser.add_argument("--n-envs", type=int, default=4, help="Number of parallel environments")
+ _ = parser.add_argument("--render-mode", type=str, default="none", help="Render mode during training")
+ _ = parser.add_argument(
"--save-path",
type=str,
default="models/ppo_AntiPendulumEnv.zip",
help="Where to save the trained model",
)
- parser.add_argument(
+ _ = parser.add_argument(
"--dry-run",
action="store_true",
help="Run 1000 steps with live reward-tracking plot, without saving the model.",
)
args = parser.parse_args()
+ logging.basicConfig(level=logging.INFO, format="%(message)s")
+
if args.dry_run:
agent = ProximalPolicyOptimizationAgent(
- AntiPendulumEnv, # type: ignore[arg-type]
+ AntiPendulumEnv,
n_envs=1,
env_kwargs={
"crane": build_crane,
@@ -47,7 +56,7 @@ def main():
else:
Path(args.save_path).parent.mkdir(parents=True, exist_ok=True)
agent = ProximalPolicyOptimizationAgent(
- AntiPendulumEnv, # type: ignore[arg-type]
+ AntiPendulumEnv,
n_envs=args.n_envs,
env_kwargs={
"crane": build_crane,
@@ -58,8 +67,8 @@ def main():
)
agent.do_training(args.steps)
vecnorm_path = Path(args.save_path).parent / f"{Path(args.save_path).stem}_vecnorm.pkl"
- print(f"Model saved to {args.save_path}")
- print(f"VecNormalize stats saved to {vecnorm_path}")
+ LOGGER.info("Model saved to %s", args.save_path)
+ LOGGER.info("VecNormalize stats saved to %s", vecnorm_path)
if __name__ == "__main__":
diff --git a/scripts/train_q.py b/scripts/train_q.py
index 1cfe8ef..26091b2 100644
--- a/scripts/train_q.py
+++ b/scripts/train_q.py
@@ -1,6 +1,9 @@
"""Train a Q-learning agent on the AntiPendulumEnv.
-Example:
+Examples
+--------
+.. code-block:: bash
+
uv run python scripts/train_q.py
uv run python scripts/train_q.py --episodes 50000 --v0 1.0 --save-path models/q_start.json
uv run python scripts/train_q.py --trained models/q_AntiPendulumEnv.json
@@ -9,38 +12,48 @@
"""
import argparse
+import logging
from pathlib import Path
from crane_controller.crane_factory import build_crane
from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
from crane_controller.q_agent import QLearningAgent
+LOGGER = logging.getLogger(__name__)
+
-def main():
+def main() -> None:
+ """Parse CLI arguments and train a Q-learning agent."""
parser = argparse.ArgumentParser(description="Train a Q-learning agent on the crane anti-pendulum task.")
- parser.add_argument("--episodes", type=int, default=10_000, help="Total training episodes")
- parser.add_argument("--v0", type=float, default=-1.0, help="Initial crane speed (negative = stop mode)")
- parser.add_argument("--reward-limit", type=float, default=-0.05, help="Per-episode reward termination threshold")
- parser.add_argument(
+ _ = parser.add_argument("--episodes", type=int, default=10_000, help="Total training episodes")
+ _ = parser.add_argument("--v0", type=float, default=-1.0, help="Initial crane speed (negative = stop mode)")
+ _ = parser.add_argument(
+ "--reward-limit", type=float, default=-0.05, help="Per-episode reward termination threshold"
+ )
+ _ = parser.add_argument(
"--save-path",
type=str,
default="models/q_AntiPendulumEnv.json",
help="Where to save the trained Q-table",
)
- parser.add_argument("--trained", type=str, default=None, help="Path to an existing Q-table JSON to continue from")
- parser.add_argument(
+ _ = parser.add_argument(
+ "--trained", type=str, default=None, help="Path to an existing Q-table JSON to continue from"
+ )
+ _ = parser.add_argument(
"--intervals",
type=int,
default=0,
help="Run interval training: N intervals of 10 episodes each (0 = disabled)",
)
- parser.add_argument(
+ _ = parser.add_argument(
"--dry-run",
action="store_true",
help="Run 50 episodes with a reward plot and no model saved, for a quick visual sanity check.",
)
args = parser.parse_args()
+ logging.basicConfig(level=logging.INFO, format="%(message)s")
+
env = AntiPendulumEnv(
build_crane,
start_speed=args.v0,
@@ -57,18 +70,18 @@ def main():
Path(args.save_path).parent.mkdir(parents=True, exist_ok=True)
agent = QLearningAgent(env, trained=(args.save_path, False))
for i in range(args.intervals):
- env.reset(seed=i + 1)
+ _ = env.reset(seed=i + 1)
agent.do_episodes(n_episodes=10)
if i == 0:
agent = QLearningAgent(env, trained=(args.save_path, True))
- print(f"Model saved to {args.save_path}")
+ LOGGER.info("Model saved to %s", args.save_path)
else:
Path(args.save_path).parent.mkdir(parents=True, exist_ok=True)
trained = (args.trained, True) if args.trained else (args.save_path, False)
agent = QLearningAgent(env, trained=trained)
agent.do_episodes(n_episodes=args.episodes, max_steps=5000)
- print(f"Model saved to {args.save_path}")
+ LOGGER.info("Model saved to %s", args.save_path)
if __name__ == "__main__":
diff --git a/src/crane_controller/__init__.py b/src/crane_controller/__init__.py
index e69de29..d5fca3c 100644
--- a/src/crane_controller/__init__.py
+++ b/src/crane_controller/__init__.py
@@ -0,0 +1 @@
+"""crane_controller package."""
diff --git a/src/crane_controller/algorithm.py b/src/crane_controller/algorithm.py
index a59e2c0..37839dd 100644
--- a/src/crane_controller/algorithm.py
+++ b/src/crane_controller/algorithm.py
@@ -1,86 +1,168 @@
+"""Algorithmic controller agent for the anti-pendulum environment."""
+
+from __future__ import annotations
+
+import logging
from itertools import product
+from typing import TYPE_CHECKING, Literal
-import gymnasium as gym
import numpy as np
from matplotlib import pyplot as plt
-from tqdm import tqdm # Progress bar
+from tqdm import tqdm
+
+if TYPE_CHECKING:
+ from collections.abc import Sequence
+
+ from crane_controller.envs.controlled_crane_pendulum import (
+ AntiPendulumEnv,
+ AntiPendulumObs,
+ )
+
+logger = logging.getLogger(__name__)
+
+START_MODE_OBSERVATION = (0, 0, 0, 0, 0)
+SHOW_TRAINING_SUMMARY = 1
+SHOW_EPISODE_ANALYSIS = 2
+
+
+def _get_moving_avgs(
+ values: Sequence[float] | np.ndarray,
+ window: int,
+ convolution_mode: Literal["valid", "same"],
+) -> np.ndarray:
+ """Compute moving averages to smooth noisy data.
+
+ Parameters
+ ----------
+ values : Sequence[float] | np.ndarray
+ Raw data series to smooth.
+ window : int
+ Number of elements in the averaging window.
+ convolution_mode : {"valid", "same"}
+ Convolution mode passed to `numpy.convolve`.
+
+ Returns
+ -------
+ np.ndarray
+ Smoothed data series.
+ """
+ return np.convolve(np.asarray(values, dtype=float).flatten(), np.ones(window), mode=convolution_mode) / window
-class AlgorithmAgent(object):
- """Agent for algorithmic control of a (simple) environment.
+class AlgorithmAgent:
+ """Agent for algorithmic control of a simple environment.
- Args:
- env (gym.Env): The Environment (class) to be controlled. Need .reset() and .step() functions.
+ Parameters
+ ----------
+ env : AntiPendulumEnv
+ The environment to be controlled. Must provide `.reset()` and `.step()` methods.
"""
envs = ("AntiPendulumEnv",)
def __init__(
self,
- env: gym.Env,
- ):
+ env: AntiPendulumEnv,
+ ) -> None:
+ """Initialize the algorithmic agent.
+
+ Parameters
+ ----------
+ env : AntiPendulumEnv
+ The environment to be controlled.
+ """
self.env = env
assert type(self.env).__name__ in AlgorithmAgent.envs, f"Environment {type(self.env).__name__} not listed."
-
# Track learning progress
self.training_error: list[float] = []
- self.combination: tuple[int, ...]
+ self.strategy: tuple[int, int, int, int] = (1, 1, 1, 1)
+
+ def get_action(self, obs: AntiPendulumObs) -> int:
+ """Choose an action based on load position and speed.
- def get_action(self, obs: tuple[int, int, bool]) -> int:
- """Choose an action based on the current observation of load position (obs[1]) and speed (obs(2)).
- The algorithmic strategy is coded as self.strategy and the observation slots 0,3,4 are ignored.
+ The algorithmic strategy is coded as ``self.strategy`` and the
+ observation slots 0, 3, 4 are ignored.
+
+ Parameters
+ ----------
+ obs : AntiPendulumObs
+ Current observation from the environment.
Returns
-------
- action: an allowed action from the action space
+ int
+ An allowed action from the action space.
+
+ Raises
+ ------
+ ValueError
+ If `obs` contains an unexpected combination of position and speed.
"""
- # print("OBS", obs)
- if obs == (0, 0, 0, 0, 0): # in start mode. Random push to get started.
- return (0, 2)[np.random.randint(0, 2)] # random 0 or 2
- else: # see the reward of all strategies at end of file. 0202 is optimal
- if obs[1] == 0 and obs[2] == 0:
+ if obs == START_MODE_OBSERVATION: # in start mode. Random push to get started.
+ return int(self.env.np_random.choice((0, 2)))
+ # see the reward of all strategies at end of file. 0202 is optimal
+ if obs[1] == 0:
+ if obs[2] == 0:
return self.strategy[0]
- elif obs[1] == 0 and obs[2] == 1:
+ if obs[2] == 1:
return self.strategy[1]
- elif obs[1] == 1 and obs[2] == 0:
- return self.strategy[2]
- elif obs[1] == 1 and obs[2] == 1:
- return self.strategy[3]
- else:
- raise ValueError("There should not be other choices {obs}") from None
-
- def do_strategies(self, max_steps: int = 5000):
- """Go through all strategies, where
+ if obs[1] == 1 and obs[2] == 0:
+ return self.strategy[2]
+ if obs[1] == 1 and obs[2] == 1:
+ return self.strategy[3]
+ raise ValueError("There should not be other choices {obs}") from None
+
+ def do_strategies(self, max_steps: int = 5000) -> None:
+ """Evaluate all strategy permutations.
+
+ Each strategy maps (position, speed) pairs to actions::
+
pos=-, speed=- => strategy[0]
pos=-, speed=+ => strategy[1]
pos=+, speed=- => strategy[2]
pos=+, speed=+ => strategy[3]
- with pos=obs[1], speed=obs[2]
+
+ where ``pos = obs[1]`` and ``speed = obs[2]``.
Observations 0, 3 and 4 are ignored.
+
+ Parameters
+ ----------
+ max_steps : int, optional
+ Maximum number of steps per episode before truncation (default 5000).
"""
- res = []
+ rewards: list[float] = []
for self.strategy in product(range(3), range(3), range(3), range(3)):
- obs, info = self.env.reset()
+ obs, _ = self.env.reset()
terminated, truncated = (False, False)
steps = 0
reward = 0.0
while not terminated and not truncated:
steps += 1
action = self.get_action(obs) # choose action (initially random, then deterministic)
- obs, _reward, terminated, truncated, info = self.env.step(action) # take action and observe result
+ obs, _reward, terminated, truncated, _ = self.env.step(action) # take action and observe result
reward = float(_reward)
if steps > max_steps:
truncated = True
- res.append(reward)
- if not self.env.render_mode == 'none':
- for i, self.strategy in enumerate(product(range(3), range(3), range(3), range(3))):
- print(f"{i}. strategy {self.strategy}: {res[i]}")
-
- def do_episodes(self, n_episodes: int = 1000, show: int = 0, max_steps: int = 1000):
- """Run episodes."""
+ rewards.append(reward)
+ for i, self.strategy in enumerate(product(range(3), range(3), range(3), range(3))):
+ logger.info("%s. strategy %s: %s", i, self.strategy, rewards[i])
+
+ def do_episodes(self, n_episodes: int = 1000, show: int = 0, max_steps: int = 1000) -> None:
+ """Run training episodes.
+
+ Parameters
+ ----------
+ n_episodes : int, optional
+ Number of episodes to run (default 1000).
+ show : int, optional
+ Visualization mode - 0 for none, 1 for training summary, 2 for
+ per-episode analysis (default 0).
+ max_steps : int, optional
+ Maximum steps per episode before truncation (default 1000).
+ """
for _episode in tqdm(range(n_episodes)):
# Start a new episode
- obs, info = self.env.reset()
+ obs, _ = self.env.reset()
terminated, truncated = (False, False)
steps = 0
reward = 0.0
@@ -88,48 +170,49 @@ def do_episodes(self, n_episodes: int = 1000, show: int = 0, max_steps: int = 10
while not terminated and not truncated:
steps += 1
action = self.get_action(obs) # choose action (initially random, then deterministic)
- # reward0 = reward
- obs, _reward, terminated, truncated, info = self.env.step(action) # take action and observe result
+ obs, _reward, terminated, truncated, _ = self.env.step(action) # take action and observe result
reward = float(_reward)
- if show == 2:
+ if show == SHOW_EPISODE_ANALYSIS:
self.analyse_episode()
if steps > max_steps:
truncated = True
- print(f"strategy {self.strategy}: {reward}")
- self.env.reset() # reset at end so that rendering is performed properly
+ logger.info("strategy %s: %s", self.strategy, reward)
+ _ = self.env.reset() # reset at end so that rendering is performed properly
- if show == 1:
+ if show == SHOW_TRAINING_SUMMARY:
self.analyse_training()
- def analyse_training(self, window: int = 500):
-
- def get_moving_avgs(arr, window, convolution_mode):
- """Compute moving average to smooth noisy data."""
- return np.convolve(np.array(arr).flatten(), np.ones(window), mode=convolution_mode) / window
+ def analyse_training(self, window: int = 500) -> None:
+ """Plot moving averages of episode rewards, lengths, and training error.
+ Parameters
+ ----------
+ window : int, optional
+ Number of episodes used for the smoothing window (default 500).
+ """
# Smooth over the given episode window
- fig, axs = plt.subplots(ncols=3, figsize=(12, 5))
+ _, axs = plt.subplots(ncols=3, figsize=(12, 5))
- lengths = [row[0] for row in self.env.reward_stats] # type: ignore ## reward_stats exist
- rewards = [row[1] for row in self.env.reward_stats] # type: ignore ## reward_stats exist
+ lengths = [row[0] for row in self.env.reward_stats]
+ rewards = [row[1] for row in self.env.reward_stats]
# Episode rewards (win/loss performance)
axs[0].set_title("Episode rewards")
- reward_moving_average = get_moving_avgs(rewards, int(window / 10), "valid")
+ reward_moving_average = _get_moving_avgs(rewards, window // 10, "valid")
axs[0].plot(range(len(reward_moving_average)), reward_moving_average)
axs[0].set_ylabel("Average Reward")
axs[0].set_xlabel("Episode")
# Episode lengths (how many actions per hand)
axs[1].set_title("Episode lengths")
- length_moving_average = get_moving_avgs(lengths, int(window / 10), "valid")
+ length_moving_average = _get_moving_avgs(lengths, window // 10, "valid")
axs[1].plot(range(len(length_moving_average)), length_moving_average)
axs[1].set_ylabel("Average Episode Length")
axs[1].set_xlabel("Episode")
# Training error (how much we're still learning)
axs[2].set_title("Training Error")
- training_error_moving_average = get_moving_avgs(self.training_error, window, "same")
+ training_error_moving_average = _get_moving_avgs(self.training_error, window, "same")
axs[2].plot(range(len(training_error_moving_average)), training_error_moving_average)
axs[2].set_ylabel("Temporal Difference Error")
axs[2].set_xlabel("Step")
@@ -137,25 +220,27 @@ def get_moving_avgs(arr, window, convolution_mode):
plt.tight_layout()
plt.show()
- def analyse_episode(self, window: int = 100):
-
- def get_moving_avgs(arr, window, convolution_mode):
- """Compute moving average to smooth noisy data."""
- return np.convolve(np.array(arr).flatten(), np.ones(window), mode=convolution_mode) / window
+ def analyse_episode(self, window: int = 100) -> None:
+ """Plot moving averages of rewards and training error for one episode.
+ Parameters
+ ----------
+ window : int, optional
+ Number of steps used for the smoothing window (default 100).
+ """
# Smooth over the given episode window
- fig, axs = plt.subplots(ncols=2, figsize=(12, 5))
+ _, axs = plt.subplots(ncols=2, figsize=(12, 5))
# Episode rewards (win/loss performance)
axs[0].set_title("Episode rewards")
- reward_moving_average = get_moving_avgs(self.env.rewards, window, "valid") # type: ignore ## rewards exist!
+ reward_moving_average = _get_moving_avgs(self.env.rewards, window, "valid")
axs[0].plot(range(len(reward_moving_average)), reward_moving_average)
axs[0].set_ylabel("Average Reward")
axs[0].set_xlabel("Episode")
# Training error (how much we're still learning)
axs[1].set_title("Training Error")
- training_error_moving_average = get_moving_avgs(self.training_error, window, "same")
+ training_error_moving_average = _get_moving_avgs(self.training_error, window, "same")
axs[1].plot(range(len(training_error_moving_average)), training_error_moving_average)
axs[1].set_ylabel("Temporal Difference Error")
axs[1].set_xlabel("Step")
@@ -163,18 +248,29 @@ def get_moving_avgs(arr, window, convolution_mode):
plt.tight_layout()
plt.show()
- def test_agent(self, num_episodes=1000):
- """Test agent performance without learning or exploration."""
- total_rewards = []
+ def test_agent(self, num_episodes: int = 1000) -> str:
+ """Test agent performance without learning or exploration.
+
+ Parameters
+ ----------
+ num_episodes : int, optional
+ Number of evaluation episodes (default 1000).
+
+ Returns
+ -------
+ str
+ Formatted summary of win rate, average reward, and standard deviation.
+ """
+ total_rewards: list[float] = []
for _ in range(num_episodes):
- obs, info = self.env.reset()
+ obs, _ = self.env.reset()
episode_reward: float = 0.0
done = False
while not done:
action = self.get_action(obs)
- obs, reward, terminated, truncated, info = self.env.step(action)
+ obs, reward, terminated, truncated, _ = self.env.step(action)
episode_reward += float(reward)
done = terminated or truncated
diff --git a/src/crane_controller/crane_factory.py b/src/crane_controller/crane_factory.py
index 20a53c2..0719f28 100644
--- a/src/crane_controller/crane_factory.py
+++ b/src/crane_controller/crane_factory.py
@@ -1,16 +1,34 @@
+"""Factory for building a simple py-crane model."""
+
import numpy as np
from py_crane.crane import Crane
def build_crane(length: float = 10.0, mass: float = 1.0, q_factor: float = 50.0) -> Crane:
+ """Build a simple crane with a pedestal boom and a flexible wire.
+
+ Parameters
+ ----------
+ length : float, optional
+ Boom and wire length (default 10.0).
+ mass : float, optional
+ Wire mass (default 1.0).
+ q_factor : float, optional
+ Quality factor controlling wire damping (default 50.0).
+
+ Returns
+ -------
+ Crane
+ Configured crane ready for simulation.
+ """
crane = Crane()
- crane.add_boom(
+ _ = crane.add_boom(
"pedestal",
description="A simple pole with same length as the wire",
mass=100.0,
boom=(length, 0.0, 0.0),
)
- crane.add_boom(
+ _ = crane.add_boom(
"wire",
description="The wire fixed to the pole. Flexible connection",
mass=mass,
diff --git a/src/crane_controller/envs/__init__.py b/src/crane_controller/envs/__init__.py
index a1e0950..e18c93c 100644
--- a/src/crane_controller/envs/__init__.py
+++ b/src/crane_controller/envs/__init__.py
@@ -1 +1 @@
-# from crane_controller.envs.grid_world import GridWorldEnv
+"""Sub-package envs."""
diff --git a/src/crane_controller/envs/controlled_crane_pendulum.py b/src/crane_controller/envs/controlled_crane_pendulum.py
index 48a0759..760aa65 100644
--- a/src/crane_controller/envs/controlled_crane_pendulum.py
+++ b/src/crane_controller/envs/controlled_crane_pendulum.py
@@ -1,5 +1,11 @@
+"""Gymnasium environment for the crane anti-pendulum task."""
+
+# mypy: disable-error-code="attr-defined"
+
+from __future__ import annotations
+
import logging
-from typing import Any
+from typing import TYPE_CHECKING, ClassVar
import gymnasium as gym
import matplotlib.pyplot as plt
@@ -7,63 +13,118 @@
from gymnasium import spaces
from py_crane.animation import AnimatePlayBackLines
+if TYPE_CHECKING:
+ from collections.abc import Callable
+
+ from matplotlib.lines import Line2D
+ from py_crane.boom import Wire
+ from py_crane.crane import Crane
+
logger = logging.getLogger(__name__)
+MIN_PLAYBACK_FRAMES = 2
+POLAR_Z_TOLERANCE = 0.1
+
-class AntiPendulumEnv(gym.Env):
- """Environment of the controlled py-crane based mobile crane,
- using the matplotlib-based animation module from py-crane.
- Simple case of anti-pendulum application.
-
- Args:
- crane (py_crane): the crane object to use as basis
- acc (float): the acceleration (-,0,+) used on the crane to perform anti-pendulum actions
- start_speed (float) = 1.0: Fixed start angle (in degrees) or
- if <0 random at every episode with [-start_speed ... start_speed]
- render_modes (str): see metadata dict
- size (int): The axis length in all directions, but -z
- seed (int): Optional possibility to use repeatable random numbers. None: not repeatable.
- reward_limit (float) = 1e-3: reward at which episodes are terminated and anti-pendulum is deemed successful.
- dt (flaot) = 0.01: time steps for the simulation
- discrete (dict): either empty dict (continuous mode) or description of discrete ranges:
- 'angles': categories of angles for the amplitude of the load,
- 'pos': current position of the load (+/- x),
- 'speed': current speed of the load (+/- x-direction)
- 'distance': distance categories from origin,
- 'sector': current sector of the crane position (+/- x)
+def _level(idx: int, val: float, categories: tuple[float, ...]) -> tuple[int, int]:
+ """Determine the bucket index for a value given ordered categories.
+
+ Parameters
+ ----------
+ idx : int
+ Fallback index returned when `val` falls below the first category.
+ val : float
+ Value to classify.
+ categories : tuple[float, ...]
+ Ordered category boundaries.
+
+ Returns
+ -------
+ tuple[int, int]
+ ``(bucket_index, error_flag)`` where *error_flag* is `idx` when the
+ value is below the first category and ``0`` otherwise.
+ """
+ if val < categories[0]:
+ return 0, idx
+ return next(
+ ((i, 0) for i, category in enumerate(categories) if val <= category),
+ (len(categories) - 1, idx),
+ )
+
+
+# Observation is either a discrete tuple or a continuous ndarray
+AntiPendulumObs = tuple[int, ...] | np.ndarray
+
+
+class AntiPendulumEnv(gym.Env[AntiPendulumObs, int]):
+ """Environment for a py-crane-based anti-pendulum task.
+
+ Uses the matplotlib-based animation module from py-crane.
+
+ Parameters
+ ----------
+ crane : Callable[..., Crane]
+ Factory callable that creates the crane object.
+ acc : float, optional
+ Acceleration magnitude applied to the crane (default 0.1).
+ start_speed : float, optional
+ Fixed start speed in degrees. A negative value causes a random speed
+ in the range ``[-|start_speed|, |start_speed|]`` each episode
+ (default 1.0).
+ render_mode : str, optional
+ One of the modes listed in ``metadata["render_modes"]``
+ (default ``"none"``).
+ size : float, optional
+ Axis length in all directions (default 10.0).
+ seed : int or None, optional
+ Seed for repeatable random numbers (default None).
+ reward_limit : float, optional
+ Reward at which an episode is terminated and the anti-pendulum is
+ deemed successful (default 50.0).
+ dt : float, optional
+ Simulation time step (default 1.0).
+ discrete : dict[str, tuple[float | int, ...]] or None, optional
+ When provided, activates discrete observation mode with the given
+ category boundaries. Expected keys: ``"angles"``, ``"pos"``,
+ ``"speed"``, ``"distance"``, ``"sector"`` (default None).
"""
- metadata = {
- "render_modes": [
+ metadata: ClassVar[dict[str, object]] = { # pyright: ignore[reportIncompatibleVariableOverride] # Gymnasium metadata typing is loose
+ "render_modes": (
"none",
"play-back",
"data",
"reward-tracking",
"plot",
"agent",
- ],
+ ),
"interval": 100,
"show-len-1": False,
"x-max": None,
}
- def __init__(
+ def __init__( # noqa: PLR0913 - environment API needs explicit parameters
self,
- crane: Any,
+ crane: Callable[..., Crane],
acc: float = 0.1,
start_speed: float = 1.0,
render_mode: str = "none",
size: float = 10.0,
seed: int | None = None,
reward_limit: float = 50.0,
- dt: float = 0.01, # time steps for the simulation
- discrete: dict[str, tuple[float, ...]] | None = None,
- ):
+ dt: float = 1.0,
+ discrete: dict[str, tuple[float | int, ...]] | None = None,
+ ) -> None:
+ """Initialize the anti-pendulum environment.
+
+ See the class docstring for parameter descriptions.
+ """
self.crane_maker = crane
- self.crane = crane()
- self.wire = self.crane.boom_by_name("wire")
- assert self.wire is not None, "Need a crane wire!"
- assert render_mode in self.metadata["render_modes"], f"render_mode: {render_mode}"
+ self.crane: Crane = crane()
+ wire = self.crane.boom_by_name("wire")
+ assert wire is not None, "Need a crane wire!"
+ self.wire: Wire = wire # type: ignore[assignment] # boom_by_name returns Boom; at runtime this is Wire
+ assert render_mode in self.metadata["render_modes"], f"render_mode: {render_mode}" # type: ignore[operator] # metadata values are typed as object
self.render_mode = render_mode
self.reward_stats: list[list[float]] = []
self._playback: list[list[float]] = []
@@ -73,21 +134,19 @@ def __init__(
elif render_mode == "plot":
self.traces: dict[str, list[float]] = {"c_x": [], "c_v": [], "l_x": [], "l_v": []}
- self.obeservation_space: spaces.Box | spaces.Discrete
- # Observations is a 4-dim np-array with
- # (crane-x, crane-v_x, load-polar-angle_x, load-v_x)
+ self.obeservation_space: spaces.Box | spaces.Discrete # pyright: ignore[reportMissingTypeArgument] # Discrete type arg not needed here
+ # Continuous observations are crane position, crane velocity, wire polar angle, and load x-velocity.
self.min_speed = 0.1 # np.sqrt(2*reward_limit) # starting with less does not make sense (goal already reached)
max_speed = np.sqrt(9.81 * self.wire.length) # speed for pendulum at +/- 90 deg. Polar as deflection from -z
if discrete is not None:
self.observation_space, self.discrete = self._init_discrete(discrete)
else:
self.discrete = {}
- self.spaces_min = np.array((-size, -max_speed, -np.pi / 2, -max_speed), float)
- self.spaces_max = np.array((size, max_speed, np.pi / 2, max_speed), float)
+ self.spaces_min = np.array((-size, -max_speed, 0.0, -max_speed), float)
+ self.spaces_max = np.array((size, max_speed, np.pi, max_speed), float)
self.observation_space = spaces.Box(self.spaces_min, self.spaces_max, shape=(4,), dtype=np.float64)
self.nresets: int = 0
- # self.reset(seed)
self.acc = acc
self.start_speed = start_speed
self.size = size
@@ -98,66 +157,89 @@ def __init__(
self.dt = dt
# We have 1 acceleration action which can each be min, zero or max, corresponding to acceleration of crane
- super().reset(seed=seed) # make sure that the environment seed is set
- self.action_space = spaces.Discrete(3, start=0, dtype=np.int64)
+ self.action_space = spaces.Discrete(3, start=0, seed=42, dtype=np.int64)
self.action_to_acc = {0: -self.acc, 1: 0.0, 2: self.acc}
+ self.steps: int = 0
+ _ = super().reset(seed=seed)
- def _init_discrete(self, spec: dict[str, tuple[float, ...]]):
- """Translate the spec of the observation space to a useful MultiDiscrete Space object.
-
- The following keys are expected
- 'angles': categories of angles for the amplitude of the load,
- 'pos': current position of the load (+/- x)
- 'speed': current speed of the load (+/- x-direction),
- 'distance': distance categories from origin,
- 'sector': current sector of the crane position (+/- x)
+ def _init_discrete(
+ self,
+ spec: dict[str, tuple[float | int, ...]],
+ ) -> tuple[spaces.MultiDiscrete, dict[str, tuple[float | int, ...]]]:
+ """Translate the observation-space spec into a MultiDiscrete space.
+
+ Expected keys in `spec`::
+
+ 'angles' - amplitude categories (converted to energy levels)
+ 'pos' - load position (+/- x)
+ 'speed' - load speed (+/- x)
+ 'distance' - distance categories from origin
+ 'sector' - crane position sector (+/- x)
+
+ Parameters
+ ----------
+ spec : dict[str, tuple[float | int, ...]]
+ Mapping of observation dimension names to category boundaries.
+
+ Returns
+ -------
+ tuple[spaces.MultiDiscrete, dict[str, tuple[float | int, ...]]]
+ The constructed ``MultiDiscrete`` space and the updated spec
+ (with ``'angles'`` replaced by ``'energies'``).
"""
# We replace the angles with pendulum energy levels, which are easier to use for observation calculation
- observation_space = spaces.MultiDiscrete(np.array([len(spec[k]) for k in spec.keys()]))
+ observation_space = spaces.MultiDiscrete(np.array([len(spec[k]) for k in spec]))
angles = spec.pop("angles")
energies = [9.81 * self.wire.length * (1.0 - np.cos(np.radians(a))) for a in angles]
- spec.update({"energies": tuple(energies)})
+ spec["energies"] = tuple(energies)
return (observation_space, spec)
- def _reward_plot_init(self, marker: str = ""):
- if not len(marker):
- point = plt.plot(0, 0)[0]
- else:
- point = plt.plot(0, 0, marker)[0]
+ def _reward_plot_init(self, marker: str = "") -> Line2D:
+ point = plt.plot(0, 0, marker)[0] if marker else plt.plot(0, 0)[0]
plt.ion()
plt.show()
return point
- def _append_playback(self, time: float):
- """Append the current state of the crane to ._playback."""
+ def _append_playback(self, time: float) -> None:
+ """Append the current crane state to the playback buffer.
+
+ Parameters
+ ----------
+ time : float
+ Current simulation time.
+ """
if not len(self._playback): # no records there yet
self._playback.append([time]) # slot 0 for time
for b in self.crane.booms():
- self._playback.append([b.end]) # one slot per boom (first boom is fixation)
+ self._playback.append([b.end]) # type: ignore[list-item] # TVector is compatible at runtime
else:
self._playback[0].append(time)
for i, b in enumerate(self.crane.booms()):
- self._playback[i + 1].append(b.end) # add vector to the correct slot.
+ self._playback[i + 1].append(b.end) # type: ignore[arg-type] # TVector is compatible at runtime
- def show_animation(self):
- """Show the playback animation of the episode (the current recording of ._playback."""
- if len(self._playback[0]) < 2 and not AntiPendulumEnv.metadata["show-len-1"]: # nothing really to show
+ def show_animation(self) -> None:
+ """Show the playback animation of the current episode recording."""
+ if len(self._playback[0]) < MIN_PLAYBACK_FRAMES and not AntiPendulumEnv.metadata["show-len-1"]:
return
- data: list[np.ndarray] = []
- for col in self._playback:
- data.append(np.array(col, float)) # make sure that we have an ndarray
+ data = [np.array(col, float) for col in self._playback]
ani = AnimatePlayBackLines(
data=data,
lw=(5, 1),
figsize=(10, 10),
- interval=AntiPendulumEnv.metadata["interval"],
+ interval=int(AntiPendulumEnv.metadata["interval"]), # type: ignore[call-overload] # metadata value is int at runtime
title="Anti-Pendulum episode",
)
ani.do_animation()
- def show_plot(self, episode):
- """Plot the report_mode='plot' figure."""
- fig, ((ax1, ax2), (ax3, _)) = plt.subplots(2, 2)
+ def show_plot(self, episode: int) -> None:
+ """Plot detailed traces for a single episode.
+
+ Parameters
+ ----------
+ episode : int
+ Episode number used in the plot title.
+ """
+ _, ((ax1, ax2), (ax3, _)) = plt.subplots(2, 2)
times = np.arange(len(self.traces["c_x"]))
damping = self.traces["l_v"][0] * np.exp(-times / self.wire.damping_time)
ax1.plot(times, self.traces["l_x"], label="load angle", color="blue")
@@ -168,39 +250,81 @@ def show_plot(self, episode):
ax2y2 = ax2.twinx()
ax2y2.plot(times, self.traces["c_v"], label="crane speed", color="red")
ax3.plot(list(range(len(self.rewards))), self.rewards, label="rewards")
- ax1.legend()
- ax2.legend()
- plt.title(f"Detailed plot of episode {episode}, reward:{self.reward}")
+ _ = ax1.legend()
+ _ = ax2.legend()
+ _ = plt.title(f"Detailed plot of episode {episode}, reward:{self.reward}")
plt.show()
- for k in self.traces.keys():
- self.traces[k] = []
+ for key in self.traces:
+ self.traces[key] = []
self.rewards = []
- def _get_obs(self) -> tuple[np.ndarray | tuple, float, int]:
- """Translate the current crane state into an observation useable by gym and calculate the related reward.
+ def _get_continuous_obs(self) -> tuple[np.ndarray, int]:
+ """Return continuous observations and an out-of-bounds error flag.
- For discrete mode:
- 'energies': categories of energies of the load,
- 'side': current side of the load (+/- x),
- 'distance': distance categories from origin,
- 'sector': current sector of the crane position (+/- x)
+ Returns
+ -------
+ tuple[np.ndarray, int]
+ ``(observation, error_flag)`` where *error_flag* is ``0`` when all
+ values are within bounds, or the 1-based index of the first
+ out-of-bounds dimension.
"""
err = 0
+ obs = np.array(
+ (
+ self.crane.position[0],
+ self.crane.velocity[0],
+ self.wire.boom[1],
+ self.wire.cm_v[0], # pyright: ignore[reportUnknownMemberType] # dynamic attr on Wire
+ ),
+ float,
+ )
+
+ for i, (_obs, _min, _max) in enumerate(zip(obs, self.spaces_min, self.spaces_max, strict=True)):
+ if not _min <= _obs <= _max:
+ err = i + 1
+
+ return obs, err
+
+ def _get_discrete_obs(self, energy: float) -> tuple[int, ...]:
+ """Return the discrete observation tuple from the current crane state.
+
+ Parameters
+ ----------
+ energy : float
+ Current pendulum energy.
- def level(idx: int, val: float, categories: tuple[float, ...]):
- """Determine the level with respect to given categories.
- If valcategories[-1] and error is flagged (leads to termination).
- """
- if val < categories[0]:
- _err = idx
- return 0
- for i, c in enumerate(categories):
- if val <= c:
- return i
- _err = idx
- return len(categories) - 1
-
- energy = 9.81 * self.wire.end[2] + 0.5 * np.dot(self.wire.cm_v, self.wire.cm_v)
+ Returns
+ -------
+ tuple[int, ...]
+ Discretised observation ``(energy_level, side, speed_sign,
+ distance_level, sector)``.
+ """
+ energy_level, _ = _level(1, energy, self.discrete["energies"])
+ distance_level, _ = _level(3, abs(self.crane.position[0]), self.discrete["distance"])
+ return (
+ energy_level,
+ int(self.wire.end[0] - self.wire.origin[0] < 0.0),
+ int(self.wire.cm_v[0] < 0.0), # pyright: ignore[reportUnknownMemberType] # dynamic attr on Wire
+ distance_level,
+ int(self.crane.position[0] < 0.0),
+ )
+
+ def _get_obs(self) -> tuple[np.ndarray | tuple[int, ...], float, int]:
+ """Compute the current observation and reward from the crane state.
+
+ In discrete mode the observation keys are::
+
+ 'energies' - energy categories of the load
+ 'side' - current side of the load (+/- x)
+ 'distance' - distance categories from origin
+ 'sector' - crane position sector (+/- x)
+
+ Returns
+ -------
+ tuple[np.ndarray | tuple[int, ...], float, int]
+ ``(observation, reward, error_flag)``.
+ """
+ energy = 9.81 * self.wire.end[2] + 0.5 * np.dot(self.wire.cm_v, self.wire.cm_v) # pyright: ignore[reportUnknownMemberType] # dynamic attr on Wire
if self.start_speed == 0.0: # start pendulum mode
reward = energy
else: # stop pendulum mode
@@ -214,49 +338,42 @@ def level(idx: int, val: float, categories: tuple[float, ...]):
obs: tuple[int, ...] | np.ndarray
if len(self.discrete):
- obs = (
- level(1, energy, self.discrete["energies"]), # energy level
- int(self.wire.end[0] - self.wire.origin[0] < 0.0), # load position >=0 / < 0 in x-direction
- int(self.wire.cm_v[0] < 0.0), # load speed >=0 / < 0 in x-direction
- level(3, abs(self.crane.position[0]), self.discrete["distance"]), # distance from origin
- int(self.crane.position[0] < 0.0), # sector with respect to origin, where the crane is
- )
+ obs = self._get_discrete_obs(energy)
+ err = 0
else:
- # polar measured downwards as +/- deflection from the negative z-axis
- polar = np.pi - self.wire.boom[1] if abs(self.wire.boom[2]) < 0.1 else -(np.pi - self.wire.boom[1])
- obs = np.array(
- (
- self.crane.position[0],
- self.crane.velocity[0],
- polar,
- self.wire.cm_v[0],
- ),
- float,
- )
-
- for i, (_obs, _min, _max) in enumerate(zip(obs, self.spaces_min, self.spaces_max, strict=True)):
- if not _min <= _obs <= _max:
- err = i + 1
+ obs, err = self._get_continuous_obs()
if self.render_mode == "plot":
self.traces["c_x"].append(self.crane.position[0])
self.traces["c_v"].append(self.crane.velocity[0])
self.traces["l_x"].append(self.wire.c_m[0])
- self.traces["l_v"].append(self.wire.cm_v[0])
+ self.traces["l_v"].append(self.wire.cm_v[0]) # pyright: ignore[reportUnknownMemberType] # dynamic attr on Wire
return (obs, reward, err)
- def low_reward(self):
+ def low_reward(self) -> float:
+ """Return the lowest possible reward for the current mode.
+
+ Returns
+ -------
+ float
+ Zero in start-pendulum mode, or the negative maximum pendulum
+ energy in stop-pendulum mode.
+ """
if self.start_speed == 0.0:
return 0.0
- else:
- return -self.discrete["energies"][-1]
+ return -float(self.discrete["energies"][-1])
- def _get_info(self, reward: float, steps: int):
+ def _get_info(self, reward: float, steps: int) -> dict[str, float | int]:
return {"steps": steps, "reward": reward}
- def reset_crane(self):
+ def reset_crane(self) -> None:
+ """Reset the crane to its initial physical state.
+
+ Sets position, velocity, torque, and force to zero, recalculates
+ statics and dynamics, and relaxes the pendulum wire.
+ """
self.crane.position = np.array((0.0, 0.0, 0.0), dtype=np.float64)
self.crane.velocity = np.array((0.0, 0.0, 0.0), dtype=np.float64)
self.crane.d_velocity = np.array((0.0, 0.0, 0.0), dtype=np.float64)
@@ -267,9 +384,26 @@ def reset_crane(self):
self.crane.calc_statics_dynamics(None)
self.wire.pendulum_relax()
- def reset(self, *, seed: int | None = None, options: dict[str, Any] | None = None):
- """Reset the crane for a new episode."""
-
+ def reset(
+ self,
+ *,
+ seed: int | None = None,
+ options: dict[str, object] | None = None,
+ ) -> tuple[tuple[int, ...] | np.ndarray, dict[str, float | int]]:
+ """Reset the environment for a new episode.
+
+ Parameters
+ ----------
+ seed : int or None, optional
+ Random seed (default None).
+ options : dict[str, object] or None, optional
+ Additional reset options (default None).
+
+ Returns
+ -------
+ tuple[tuple[int, ...] | np.ndarray, dict[str, float | int]]
+ Initial observation and info dict.
+ """
self.reset_crane()
if self.nresets <= 0: # reset during instantiation. Initialize
@@ -287,52 +421,65 @@ def reset(self, *, seed: int | None = None, options: dict[str, Any] | None = Non
elif self.render_mode == "plot":
self.show_plot(self.nresets)
- super().reset(seed=seed, options=options)
+ _ = super().reset(seed=seed, options=options)
self.nresets += 1
if self.start_speed == 0.0: # run in 'start' mode, learning how to start the pendulum action
- assert self.wire.cm_v[0] == 0.0, f"Load speed expected zero. Found {self.wire.cm_v[0]}"
+ assert self.wire.cm_v[0] == 0.0, f"Load speed expected zero. Found {self.wire.cm_v[0]}" # pyright: ignore[reportUnknownMemberType] # dynamic attr on Wire
elif self.start_speed < 0.0: # random speed in 'stop' mode [-,+] range
speed = self.np_random.uniform(
-(-self.start_speed - self.min_speed),
(-self.start_speed - self.min_speed),
)
- self.wire.cm_v[0] = speed + self.min_speed if speed >= 0 else speed - self.min_speed
+ speed = speed + self.min_speed if speed >= 0 else speed - self.min_speed
+ self.wire.cm_v[0] = np.radians(speed) # pyright: ignore[reportUnknownMemberType] # dynamic attr on Wire
else: # fixed speed in 'stop' mode (more control)
- self.wire.cm_v[0] = self.start_speed
+ self.wire.cm_v[0] = np.radians(self.start_speed) # pyright: ignore[reportUnknownMemberType] # dynamic attr on Wire
obs, self.reward, _ = self._get_obs()
if self.render_mode == "play-back":
self._append_playback(0.0)
- self.steps: int = 0
+ self.steps = 0
info = self._get_info(self.reward, self.steps)
return obs, info
- def step(self, action):
- self.crane.d_velocity[0] = self.action_to_acc[int(action)]
+ def step(self, action: int) -> tuple[tuple[int, ...] | np.ndarray, float, bool, bool, dict[str, float | int]]:
+ """Advance the environment by one time step.
+
+ Parameters
+ ----------
+ action : int
+ Action index selecting the crane acceleration.
+
+ Returns
+ -------
+ tuple[tuple[int, ...] | np.ndarray, float, bool, bool, dict[str, float | int]]
+ ``(observation, reward, terminated, truncated, info)``.
+ """
+ action_idx = action
+ if action_idx not in self.action_to_acc:
+ action_idx += 1
+ self.crane.d_velocity[0] = self.action_to_acc[action_idx]
self.steps += 1
- self.crane.do_step(self.steps, self.dt)
+ _ = self.crane.do_step(self.steps, self.dt)
obs, self.reward, truncated = self._get_obs()
- # print(f"@{self.steps}({action-1}) => {self.reward:4.3f}. Crane:{obs[0]:4.3f},{obs[1]:4.3f}. load:{np.degrees(obs[2]):4.3f},{obs[3]:4.3f}")
if self.render_mode != "none":
self.rewards.append(self.reward)
if self.render_mode == "play-back":
self._append_playback(self.steps)
elif self.render_mode == "reward-tracking":
- self._reward_point.set_data(list(range(len(self.rewards))), self.rewards)
- plt.xlim((0, len(self.rewards)))
- plt.ylim((min(self.rewards), max(self.rewards)))
+ _ = self._reward_point.set_data(list(range(len(self.rewards))), self.rewards)
+ _ = plt.xlim((0, len(self.rewards)))
+ _ = plt.ylim((min(self.rewards), max(self.rewards)))
plt.pause(1e-10)
terminated = self.reward > self.reward_limit
if terminated:
self.nsuccess += 1
info = self._get_info(self.reward, self.steps)
- # if terminated or truncated>0:
- # print(f"RESET@{self.steps}. {self.reward}, {self.reward}. Reason:{'terminated' if terminated else truncated}. Obs:{obs}")
return obs, self.reward, terminated, (truncated > 0), info
- def render(self):
- """Render a frame."""
+ def render(self) -> None:
+ """Render the current episode."""
if self.render_mode == "play-back": # show the animation
self.show_animation()
diff --git a/src/crane_controller/envs/controlled_mobile_crane.py b/src/crane_controller/envs/controlled_mobile_crane.py
index 040b7d1..afa142e 100644
--- a/src/crane_controller/envs/controlled_mobile_crane.py
+++ b/src/crane_controller/envs/controlled_mobile_crane.py
@@ -1,30 +1,57 @@
-from typing import Any
+"""Gymnasium environment for a mobile crane grid-navigation task."""
+
+# mypy: disable-error-code="override"
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, ClassVar
import gymnasium as gym
import numpy as np
+import numpy.typing as npt
import pygame
from gymnasium import spaces
-from py_crane.crane import Crane
+
+if TYPE_CHECKING:
+ from py_crane.crane import Crane
+
+# Type aliases for observations and actions
+CraneObs = dict[str, npt.NDArray[np.int_]]
class Actions:
- def __init__(self, mode: str):
- pass
+ """Container for the movement mode of a crane environment."""
+ def __init__(self, mode: str) -> None:
+ """Initialize the action container.
+
+ Parameters
+ ----------
+ mode : str
+ Movement mode, e.g. ``"separate"`` or ``"combined"``.
+ """
+ self.mode = mode
-class ControlledCraneEnv(gym.Env):
- """Environment of the controlled py-crane based mobile crane,
- using the matplotlib-based animation module from py-crane.
- Args:
- crane (py_crane): the crane object to use as basis
- render_modes (str): 'animation' (use direct animation) or 'data' (return boom.end for all booms)
- size (int): The axis length in all directions, but -z
+class ControlledCraneEnv(gym.Env[CraneObs, int]):
+ """Environment for a py-crane-based mobile crane.
+ Uses the matplotlib-based animation module from py-crane.
+ Parameters
+ ----------
+ crane : Crane
+ The crane object to control.
+ mov_mode : str, optional
+ Movement mode - ``"separate"`` or ``"combined"`` (default ``"separate"``).
+ render_mode : str or None, optional
+ ``"animation"`` for direct animation or ``"data"`` to return boom-end
+ arrays (default None).
+ size : int, optional
+ Grid axis length in all directions (default 10).
"""
- metadata = {"render_modes": ["animation", "data"], "render_fps": 4}
+ metadata: ClassVar[dict[str, object]] = {"render_modes": ["animation", "data"], "render_fps": 4} # pyright: ignore[reportIncompatibleVariableOverride]
def __init__(
self,
@@ -32,7 +59,12 @@ def __init__(
mov_mode: str = "separate",
render_mode: str | None = None,
size: int = 10,
- ):
+ ) -> None:
+ """Initialize the mobile crane environment.
+
+ See the class docstring for parameter descriptions.
+ """
+ self.crane = crane
self.mode: int = 1 if mov_mode == "separate" else 2
self.size = size
self.figsize: tuple[int, int] = (15, 15) # The matplotlib animation window
@@ -53,10 +85,7 @@ def __init__(
# boom length
# wire length
# Coded as integer if 'separate' and MultiDiscrete else.
- if self.mode == 0:
- self.action_space = spaces.Discrete(5, start=-4, seed=42)
- else:
- self.action_space = spaces.MultiDiscrete(np.array((3, 3, 3, 3), int), seed=42)
+ self.action_space = spaces.MultiDiscrete(np.array((3, 3, 3, 3), int), seed=42) # type: ignore[assignment] # MultiDiscrete is compatible with Space[int]
"""
The following dictionary maps abstract actions from `self.action_space` to
@@ -70,7 +99,7 @@ def __init__(
3: np.array([0, -1]), # down
}
- assert render_mode is None or render_mode in self.metadata["render_modes"]
+ assert render_mode is None or render_mode in self.metadata["render_modes"] # type: ignore[operator] # metadata values are typed as object
self.render_mode = render_mode
"""
@@ -80,18 +109,39 @@ def __init__(
human-mode. They will remain `None` until human-mode is used for the
first time.
"""
- self.window: Any = None
+ self.window: pygame.Surface | None = None
self.clock: pygame.time.Clock | None = None
+ self._agent_location: npt.NDArray[np.int_] = np.zeros(2, dtype=int)
+ self._target_location: npt.NDArray[np.int_] = np.zeros(2, dtype=int)
- def _get_obs(self):
+ def _get_obs(self) -> dict[str, npt.NDArray[np.int_]]:
return {"agent": self._agent_location, "target": self._target_location}
- def _get_info(self):
- return {"distance": np.linalg.norm(self._agent_location - self._target_location, ord=1)}
+ def _get_info(self) -> dict[str, float]:
+ return {"distance": float(np.linalg.norm(self._agent_location - self._target_location, ord=1))}
- def reset(self, *, seed: int | None = None, options: dict[str, Any] | None = None):
+ def reset(
+ self,
+ *,
+ seed: int | None = None,
+ options: dict[str, object] | None = None,
+ ) -> tuple[dict[str, npt.NDArray[np.int_]], dict[str, float]]:
+ """Reset the environment for a new episode.
+
+ Parameters
+ ----------
+ seed : int or None, optional
+ Random seed for reproducibility (default None).
+ options : dict[str, object] or None, optional
+ Additional reset options (default None).
+
+ Returns
+ -------
+ tuple[dict[str, npt.NDArray[np.int_]], dict[str, float]]
+ ``(observation, info)`` with agent/target locations and distance.
+ """
# We need the following line to seed self.np_random
- super().reset(seed=seed, options=options)
+ _ = super().reset(seed=seed, options=options)
# Choose the agent's location uniformly at random
self._agent_location = self.np_random.integers(0, self.size, size=2, dtype=int)
@@ -106,11 +156,23 @@ def reset(self, *, seed: int | None = None, options: dict[str, Any] | None = Non
info = self._get_info()
if self.render_mode == "animation":
- self._render_frame()
+ _ = self._render_frame()
return observation, info
- def step(self, action):
+ def step(self, action: int) -> tuple[dict[str, npt.NDArray[np.int_]], int, bool, bool, dict[str, float]]:
+ """Advance the environment by one step.
+
+ Parameters
+ ----------
+ action : int
+ Action index from ``{0, 1, 2, 3}`` selecting a movement direction.
+
+ Returns
+ -------
+ tuple[dict[str, npt.NDArray[np.int_]], int, bool, bool, dict[str, float]]
+ ``(observation, reward, terminated, truncated, info)``.
+ """
# Map the action (element of {0,1,2,3}) to the direction we walk in
direction = self._action_to_direction[action]
# We use `np.clip` to make sure we don't leave the grid
@@ -122,35 +184,41 @@ def step(self, action):
info = self._get_info()
if self.render_mode == "animation":
- self._render_frame()
+ _ = self._render_frame()
return observation, reward, terminated, False, info
- def render(self):
- if self.render_mode == "data":
- return self._render_frame()
+ def render(self) -> npt.NDArray[np.uint8] | None: # NDArray is compatible with RenderFrame
+ """Render the current environment state.
- def _render_frame(self):
+ Returns
+ -------
+ npt.NDArray[np.uint8] or None
+ Pixel array in ``"data"`` mode, ``None`` otherwise.
+ """
+ return self._render_frame() if self.render_mode == "data" else None
+
+ def _render_frame(self) -> npt.NDArray[np.uint8] | None:
if self.window is None and self.render_mode == "animation":
- pygame.init()
+ _ = pygame.init()
pygame.display.init()
self.window = pygame.display.set_mode((self.size, self.size))
if self.clock is None and self.render_mode == "animation":
self.clock = pygame.time.Clock()
canvas = pygame.Surface((self.size, self.size))
- canvas.fill((255, 255, 255))
+ _ = canvas.fill((255, 255, 255))
pix_square_size = self.size / self.size # The size of a single grid square in pixels
# First we draw the target
_x, _y = pix_square_size * self._target_location
- pygame.draw.rect(
+ _ = pygame.draw.rect(
canvas,
(255, 0, 0),
pygame.Rect(_x, _y, pix_square_size, pix_square_size),
)
# Now we draw the agent
- pygame.draw.circle(
+ _ = pygame.draw.circle(
canvas,
(0, 0, 255),
tuple((self._agent_location + 0.5) * pix_square_size),
@@ -159,14 +227,14 @@ def _render_frame(self):
# Finally, add some gridlines
for x in range(self.size + 1):
- pygame.draw.line(
+ _ = pygame.draw.line(
canvas,
0,
(0, pix_square_size * x),
(self.size, pix_square_size * x),
width=3,
)
- pygame.draw.line(
+ _ = pygame.draw.line(
canvas,
0,
(pix_square_size * x, 0),
@@ -177,7 +245,7 @@ def _render_frame(self):
if self.render_mode == "animation":
# The following line copies our drawings from `canvas` to the visible window
assert self.window is not None
- self.window.blit(canvas, canvas.get_rect())
+ _ = self.window.blit(canvas, canvas.get_rect())
pygame.event.pump()
pygame.display.update()
@@ -185,11 +253,17 @@ def _render_frame(self):
# The following line will automatically add a delay to
# keep the framerate stable.
assert self.clock is not None
- self.clock.tick(self.metadata["render_fps"])
- else: # rgb_array
+ self.clock.tick(self.metadata["render_fps"]) # type: ignore[arg-type] # metadata value is float at runtime
+ return None
+
+ # data mode
+ if self.render_mode == "data":
return np.transpose(np.array(pygame.surfarray.pixels3d(canvas)), axes=(1, 0, 2))
- def close(self):
+ return None
+
+ def close(self) -> None:
+ """Clean up Pygame resources."""
if self.window is not None:
pygame.display.quit()
pygame.quit()
diff --git a/src/crane_controller/ppo_agent.py b/src/crane_controller/ppo_agent.py
index 3ad31e2..d54251e 100644
--- a/src/crane_controller/ppo_agent.py
+++ b/src/crane_controller/ppo_agent.py
@@ -1,57 +1,79 @@
+"""PPO-based agent for the anti-pendulum environment."""
+
from __future__ import annotations
+import logging
from pathlib import Path
-from typing import Any
+from typing import TYPE_CHECKING, Any
-import gymnasium as gym
import matplotlib.pyplot as plt
+import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.vec_env import VecNormalize
-plt.rcParams["figure.figsize"] = (10, 5)
+if TYPE_CHECKING:
+ from collections.abc import Callable
+ from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
-class ProximalPolicyOptimizationAgent:
- """`PPO algorithm _vecnorm.pkl`.
- Args:
- env (gym.Env): the environment the agent is acting on.
- n_envs (int) = 4: The number of environments to used during training.
- n_envs=0 signals that the trained agent should be loaded from file.
- env_kwargs (dict): Optional possibility to provide additional kwargs for environment
- trained (str|Path): Optional file name for saving/loading the trained agent. Required for n_envs=0
+class ProximalPolicyOptimizationAgent:
+ """Agent that learns a policy via the PPO algorithm.
+
+ `PPO algorithm `_.
+
+ PPO agents can be saved as a zip file and re-loaded to avoid re-training.
+ VecNormalize statistics are saved alongside the model as ``_vecnorm.pkl``.
+
+ Parameters
+ ----------
+ env : Callable[..., AntiPendulumEnv]
+ Factory callable that creates the environment.
+ n_envs : int, optional
+ Number of parallel environments used during training.
+ ``n_envs=0`` signals that a pre-trained agent should be loaded from
+ file (default 4).
+ env_kwargs : dict[str, Any] or None, optional
+ Additional keyword arguments forwarded to the environment factory
+ (default None).
+ trained : tuple[str | Path, bool] or None, optional
+ File name and save/load flag for the trained agent. Required when
+ ``n_envs=0`` (default None).
"""
def __init__(
self,
- env: gym.Env,
+ env: Callable[..., AntiPendulumEnv],
n_envs: int = 4,
env_kwargs: dict[str, Any] | None = None,
- trained: tuple[str, bool] | None = None,
- ):
+ trained: tuple[str | Path, bool] | None = None,
+ ) -> None:
+ """Initialize the PPO agent.
+
+ See the class docstring for parameter descriptions.
+ """
self.trained = trained
inference_only = n_envs <= 0
_n_envs = 1 if inference_only else n_envs
- raw_vec_env = make_vec_env(env_id=env, n_envs=_n_envs, env_kwargs=env_kwargs) # type: ignore
+ raw_vec_env = make_vec_env(env_id=env, n_envs=_n_envs, env_kwargs=env_kwargs)
if inference_only:
- assert trained is not None, "When no training is specified a saved model should be provided"
- stats_path = self._stats_path(trained[0])
+ assert self.trained is not None, "When no training is specified a saved model should be provided"
+ stats_path = self._stats_path(str(self.trained[0]))
if stats_path.exists():
self.vec_env = VecNormalize.load(str(stats_path), raw_vec_env)
else:
self.vec_env = VecNormalize(raw_vec_env, norm_obs=True, norm_reward=False)
self.vec_env.training = False
self.vec_env.norm_reward = False
- self.model = PPO.load(trained[0], env=self.vec_env)
+ self.model = PPO.load(str(self.trained[0]), env=self.vec_env)
else:
self.vec_env = VecNormalize(raw_vec_env, norm_obs=True, norm_reward=True)
if _n_envs == 1:
@@ -59,48 +81,73 @@ def __init__(
else:
self.model = PPO("MlpPolicy", self.vec_env)
self.trained = (
- trained[0] if trained is not None else f"ppo_{env.__name__}", # type: ignore[attr-defined]
+ trained[0] if trained is not None else f"ppo_{env.__name__}",
False if trained is None else trained[1],
)
# Single unwrapped env for do_one_episode/evaluate without reconstructing a new crane.
- self.env = self.vec_env.venv.envs[0] # type: ignore[attr-defined]
+ # Assumes VecNormalize wraps a DummyVecEnv (stable-baselines3 default), which exposes .venv.envs.
+ self.env: AntiPendulumEnv = self.vec_env.venv.envs[0] # type: ignore[attr-defined]
@staticmethod
def _stats_path(model_path: str) -> Path:
+ """Return the path for the VecNormalize statistics file.
+
+ Parameters
+ ----------
+ model_path : str
+ Path to the model zip file.
+
+ Returns
+ -------
+ Path
+ Path to the ``_vecnorm.pkl`` statistics file alongside the model.
+ """
p = Path(model_path)
return p.parent / f"{p.stem}_vecnorm.pkl"
- def do_training(self, total_timesteps: int = 25000, progress_bar: bool = True):
- self.model.learn(total_timesteps, progress_bar=progress_bar)
- if self.trained is not None and self.trained[1] and self.env.render_mode not in ("play-back",):
- self.model.save(self.trained[0])
- self.vec_env.save(str(self._stats_path(self.trained[0])))
-
- def evaluate(self, n_episodes: int = 10):
+ def do_training(self, total_timesteps: int = 25000, *, progress_bar: bool = True) -> None:
+ """Train the PPO model.
+
+ Parameters
+ ----------
+ total_timesteps : int, optional
+ Number of training timesteps (default 25000).
+ progress_bar : bool, optional
+ Whether to display a progress bar during training (default True).
+ """
+ _ = self.model.learn(total_timesteps, progress_bar=progress_bar)
+ if self.trained is not None and self.trained[1] and self.env.render_mode != "play-back":
+ self.model.save(str(self.trained[0]))
+ self.vec_env.save(str(self._stats_path(str(self.trained[0]))))
+
+ def evaluate(self, n_episodes: int = 10) -> None:
+ """Evaluate the trained policy and log results.
+
+ Parameters
+ ----------
+ n_episodes : int, optional
+ Number of evaluation episodes (default 10).
+ """
self.vec_env.training = False
self.vec_env.norm_reward = False
mean_reward, std_reward = evaluate_policy(self.model, self.vec_env, n_eval_episodes=n_episodes)
self.vec_env.training = True
self.vec_env.norm_reward = True
- print(f"Mean:{mean_reward}, stdev:{std_reward}")
+ logger.info("Mean:%s, stdev:%s", mean_reward, std_reward)
+
+ def do_one_episode(self, seed: int = 1) -> None:
+ """Run one episode on the non-vectorised, trained environment.
- def do_one_episode(self, seed: int = 1):
- """Do one episode using the trained normalizer for observations."""
- obs, info = self.env.reset(seed=seed)
+ Parameters
+ ----------
+ seed : int, optional
+ Random seed for the environment reset (default 1).
+ """
+ obs, _ = self.env.reset(seed=seed)
terminated = truncated = False
while not terminated and not truncated:
- norm_obs = self.vec_env.normalize_obs(obs)
- action, _states = self.model.predict(norm_obs, deterministic=True)
- obs, rewards, terminated, truncated, info = self.env.step(action)
+ norm_obs = self.vec_env.normalize_obs(np.asarray(obs))
+ action, _states = self.model.predict(np.asarray(norm_obs), deterministic=True)
+ obs, _rewards, terminated, truncated, _ = self.env.step(int(action))
self.env.render()
-
-
-# class TrainingMonitor(BaseCallback):
-# def __init__(self, verbose:int=0):
-# super().__init__(verbose)
-#
-# def _on_step(self):
-# """Called at every step of the training."""
-#
-# return True
diff --git a/src/crane_controller/q_agent.py b/src/crane_controller/q_agent.py
index ca9add7..27ac78e 100644
--- a/src/crane_controller/q_agent.py
+++ b/src/crane_controller/q_agent.py
@@ -1,31 +1,75 @@
+"""Q-learning agent for the anti-pendulum environment."""
+
+from __future__ import annotations
+
import json
import logging
from ast import literal_eval
from collections import defaultdict
from pathlib import Path
+from typing import TYPE_CHECKING, ClassVar, Literal
-import gymnasium as gym
import numpy as np
from matplotlib import pyplot as plt
-from tqdm import tqdm # Progress bar
+from tqdm import tqdm
-logger = logging.getLogger(__name__)
+if TYPE_CHECKING:
+ from collections.abc import Sequence
+ from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
-class QLearningAgent(object):
- """Agent for training the controller (a Gym Environment).
+logger = logging.getLogger(__name__)
- Args:
- env (gym.Env): The Environment (class) to be trained. Need .reset() and .step() functions.
- learning_rate (float): How quickly to update Q-values (0-1)
- initial_epsilon (float): Starting exploration rate (usually 1.0)
- final_epsilon (float): Minimum exploration rate (usually 0.1)
- discount_factor (float): How much to value future rewards (0-1)
- trained (tuple[str,bool]): Optional possibility to save q_values after training / read pre-trained q_values:
- (filename,use-it): (filename,False): perform new training and save, (filename,True) use pre-trained values
+SHOW_TRAINING_SUMMARY = 1
+SHOW_EPISODE_ANALYSIS = 2
+
+
+def _get_moving_avgs(
+ values: Sequence[float] | np.ndarray,
+ window: int,
+ convolution_mode: Literal["valid", "same"],
+) -> np.ndarray:
+ """Compute moving averages to smooth noisy data.
+
+ Parameters
+ ----------
+ values : Sequence[float] | np.ndarray
+ Raw data series to smooth.
+ window : int
+ Number of elements in the averaging window.
+ convolution_mode : {"valid", "same"}
+ Convolution mode passed to `numpy.convolve`.
+
+ Returns
+ -------
+ np.ndarray
+ Smoothed data series.
+ """
+ return np.convolve(np.asarray(values, dtype=float).flatten(), np.ones(window), mode=convolution_mode) / window
+
+
+class QLearningAgent:
+ """Agent for training a controller via Q-learning.
+
+ Parameters
+ ----------
+ env : AntiPendulumEnv
+ The environment to be trained. Must provide `.reset()` and `.step()` methods.
+ learning_rate : float, optional
+ How quickly to update Q-values, in the range (0, 1] (default 0.1).
+ initial_epsilon : float, optional
+ Starting exploration rate (default 1.0).
+ final_epsilon : float, optional
+ Minimum exploration rate (default 0.1).
+ discount_factor : float, optional
+ How much to value future rewards, in the range [0, 1] (default 0.95).
+ trained : tuple[str | Path, bool] or None, optional
+ Optional path and flag for pre-trained Q-values.
+ ``(filename, False)`` trains from scratch and saves;
+ ``(filename, True)`` loads pre-trained values (default None).
"""
- DEFAULT_DISCRETE: dict = {
+ DEFAULT_DISCRETE: ClassVar[dict[str, tuple[float | int, ...]]] = {
"angles": (0.0, 1.0, 5.0, 10.0, 20.0, 30.0, 90.0),
"pos": (0, 1),
"speed": (0, 1),
@@ -35,24 +79,26 @@ class QLearningAgent(object):
def __init__(
self,
- env: gym.Env,
+ env: AntiPendulumEnv,
learning_rate: float = 0.1,
initial_epsilon: float = 1.0,
final_epsilon: float = 0.1,
discount_factor: float = 0.95,
trained: tuple[str | Path, bool] | None = None,
- ):
+ ) -> None:
+ """Initialize the Q-learning agent.
+
+ See the class docstring for parameter descriptions.
+ """
self.env = env
- # print("ACTION_SPACE.N", env.action_space.n, defaultdict( lambda: np.zeros(env.action_space.n))['xx'])
- # Q-table: maps (state, action) to expected reward
- # defaultdict automatically creates entries with zeros for new states
_filename, self.use_pre_trained = trained if trained is not None else (None, False)
self.filename: Path | None = Path(_filename) if _filename is not None else None
+ self.q_values: defaultdict[tuple[int, ...], np.ndarray]
if self.use_pre_trained and self.filename is not None:
self.q_values = self.read_dumped(self.filename)
self.epsilon = final_epsilon # assume that we are fully learned
else: # start from scratch, but save the q_values afterwards
- self.q_values = defaultdict(lambda: np.array((0.0,) * env.action_space.n, float)) # type: ignore ## n!
+ self.q_values = defaultdict(lambda: np.array((0.0,) * env.action_space.n, float)) # type: ignore[attr-defined,type-var]
self.epsilon = initial_epsilon # start from scratch
self.lr = learning_rate
@@ -64,49 +110,70 @@ def __init__(
# Track learning progress
self.training_error: list[float] = []
- def analyse_q(self, obs: tuple[int, ...]):
+ def analyse_q(self, obs: tuple[int, ...]) -> None:
+ """Log Q-table entries matching an observation pattern.
+
+ Uses ``-1`` as a wildcard in the observation tuple to match any value
+ in that dimension.
+
+ Parameters
+ ----------
+ obs : tuple[int, ...]
+ Observation filter pattern. Dimensions set to ``-1`` match all.
+ """
for comb, q in self.q_values.items():
- include = True
- for c, o in zip(comb, obs, strict=True):
- if o >= 0 and o != c:
- include = False
- break
+ include = not any(o >= 0 and o != c for c, o in zip(comb, obs, strict=True))
if include:
- print(comb, q, int(np.argmax(q)), np.average(q), np.std(q) / np.average(q))
+ logger.info("%s %s %s %s %s", comb, q, int(np.argmax(q)), np.average(q), np.std(q) / np.average(q))
- def get_action(self, obs: tuple[int, int, bool]) -> int:
+ def get_action(self, obs: tuple[int, ...]) -> int:
"""Choose an action using epsilon-greedy strategy.
+ Parameters
+ ----------
+ obs : tuple[int, ...]
+ Current discretised observation.
+
Returns
-------
- action: 0 (stand) or 1 (hit)
+ int
+ Selected action index.
"""
- if np.random.random() < self.epsilon: # With probability epsilon: explore (random action)
- return self.env.action_space.sample()
- else: # With probability (1-epsilon): exploit (best known action)
- return int(np.argmax(self.q_values[obs]))
+ if self.env.np_random.random() < self.epsilon:
+ return int(self.env.action_space.sample())
+ # With probability (1-epsilon): exploit (best known action)
+ return int(np.argmax(self.q_values[obs]))
def update_q(
self,
- obs: gym.Space,
+ obs: tuple[int, ...],
action: int,
reward: float,
+ *,
terminated: bool,
- next_obs: gym.Space,
- ):
+ next_obs: tuple[int, ...],
+ ) -> None:
"""Update Q-value based on experience.
- self.update(obs, action, reward, terminated, next_obs) # learn from this experience.
-
- This is the heart of Q-learning: learn from (state, action, reward, next_state).
-
- See also `Q-learning `_
- Args:
- obs: previous observed state
- action: action performed on the state 'obs'
- reward: the reward from 'action'
- next_obs: the new observed state after 'action' on state 'obs'
- terminated: info whether the agent was terminated after 'action'
+ This is the heart of Q-learning: learn from
+ (state, action, reward, next_state).
+
+ See Also
+ --------
+ `Q-learning `_
+
+ Parameters
+ ----------
+ obs : tuple[int, ...]
+ Previous observed state.
+ action : int
+ Action performed in state `obs`.
+ reward : float
+ Reward received after taking `action`.
+ terminated : bool
+ Whether the episode ended after `action`.
+ next_obs : tuple[int, ...]
+ New observed state after `action`.
"""
# What's the best we could do from the next state? Zero if episode terminated.
future_q_value = (not terminated) * np.max(self.q_values[next_obs])
@@ -123,43 +190,66 @@ def update_q(
# Track learning progress (useful for debugging)
self.training_error.append(temporal_difference)
- def do_episodes(self, n_episodes: int = 1000, max_steps: int = 5000, show: int = 0):
- """Do n_episodes, using pre-trained q_values or starting a new training sequence."""
+ def do_episodes(self, n_episodes: int = 1000, max_steps: int = 5000, show: int = 0) -> None:
+ """Run training or evaluation episodes.
+
+ Uses pre-trained Q-values when available, otherwise starts a new
+ training sequence.
+
+ Parameters
+ ----------
+ n_episodes : int, optional
+ Number of episodes to run (default 1000).
+ max_steps : int, optional
+ Maximum steps per episode before truncation (default 5000).
+ show : int, optional
+ Visualization mode - 0 for none, 1 for training summary, 2 for
+ per-episode analysis (default 0).
+ """
if self.use_pre_trained:
- logger.info(f"Starting {n_episodes} episodes, using pre-trained values from {self.filename}")
+ logger.info("Starting %s episodes, using pre-trained values from %s", n_episodes, self.filename)
else:
- logger.info(f"Starting new training with {n_episodes} episodes.")
+ logger.info("Starting new training with %s episodes.", n_episodes)
for _episode in tqdm(range(n_episodes)):
# Start a new episode
- obs, info = self.env.reset()
+ obs, _ = self.env.reset()
+ assert isinstance(obs, tuple)
nsteps = 0
terminated, truncated = (False, False)
while not terminated and not truncated:
action = self.get_action(obs) # choose action (initially random, gradually more intelligent)
- next_obs, _reward, terminated, truncated, info = self.env.step(action) # take action and observe result
+ next_obs, _reward, terminated, truncated, _ = self.env.step(action) # take action and observe result
+ assert isinstance(next_obs, tuple)
reward = float(_reward)
- self.update_q(obs, action, reward, terminated, next_obs) # learn from this experience
+ self.update_q(obs, action, reward, terminated=terminated, next_obs=next_obs)
# Move to next state
obs = next_obs
- if show == 2:
+ if show == SHOW_EPISODE_ANALYSIS:
self.analyse_episode()
nsteps += 1
truncated |= nsteps > max_steps
# Reduce exploration rate (agent becomes less random over time):
self.epsilon = max(self.final_epsilon, self.epsilon - self.epsilon / (n_episodes / 2))
- if show == 1:
+ if show == SHOW_TRAINING_SUMMARY:
self.analyse_training()
if self.filename:
self.dump_results()
- def dump_results(self, filename: str | Path = ""):
- """Dump the q_values to a json file."""
+ def dump_results(self, filename: str | Path = "") -> None:
+ """Dump the Q-values to a JSON file.
+
+ Parameters
+ ----------
+ filename : str or Path, optional
+ Target file path. When empty, the filename provided at
+ construction time is used (default "").
+ """
if not filename: # automatic file name
if self.filename is None:
logger.warning("No base file name provided. Aborting dump to file.")
return
- elif self.use_pre_trained: # do not overwrite pre-trained data
+ if self.use_pre_trained: # do not overwrite pre-trained data
if len(self.filename.stem.split("_")) == 1:
_filename = self.filename.parent / f"{self.filename.stem}_1{self.filename.suffix}"
else:
@@ -170,51 +260,67 @@ def dump_results(self, filename: str | Path = ""):
else:
_filename = Path(filename)
- converted: dict[str, list] = {}
+ converted: dict[str, list[float]] = {}
for k, v in self.q_values.items():
- converted.update({str(k): list(v) if isinstance(v, np.ndarray) else v})
- with open(_filename, "w") as _f:
+ converted |= {str(k): list(v)}
+ with _filename.open("w", encoding="utf-8") as _f:
json.dump(converted, _f, indent=3)
- logger.info(f"Updated q_values saved to {_filename.resolve()}")
+ logger.info("Updated q_values saved to %s", _filename.resolve())
+
+ def read_dumped(self, filename: str | Path) -> defaultdict[tuple[int, ...], np.ndarray]:
+ """Read a Q-values dict from a JSON file.
- def read_dumped(self, filename: str | Path):
- """Read a q_values dict (saved as json) from file."""
- with open(filename, "r") as _f:
+ Parameters
+ ----------
+ filename : str or Path
+ Path to the JSON file containing saved Q-values.
+
+ Returns
+ -------
+ defaultdict[tuple[int, ...], np.ndarray]
+ Loaded Q-values mapping observation tuples to action-value arrays.
+ """
+ path = Path(filename)
+ with path.open(encoding="utf-8") as _f:
from_dump = json.load(_f)
- q_values = defaultdict(lambda: np.array((0.0,) * self.env.action_space.n, float)) # type: ignore # n exists
+ q_values: defaultdict[tuple[int, ...], np.ndarray] = defaultdict(
+ lambda: np.array((0.0,) * self.env.action_space.n, float) # type: ignore[attr-defined,type-var]
+ )
for k, v in from_dump.items():
q_values.update({literal_eval(k): np.array(v) if isinstance(v, list) else v})
return q_values
- def analyse_training(self, window: int = 500):
-
- def get_moving_avgs(arr, window, convolution_mode):
- """Compute moving average to smooth noisy data."""
- return np.convolve(np.array(arr).flatten(), np.ones(window), mode=convolution_mode) / window
+ def analyse_training(self, window: int = 500) -> None:
+ """Plot moving averages of episode rewards, lengths, and training error.
+ Parameters
+ ----------
+ window : int, optional
+ Number of episodes used for the smoothing window (default 500).
+ """
# Smooth over the given episode window
- fig, axs = plt.subplots(ncols=3, figsize=(12, 5))
+ _, axs = plt.subplots(ncols=3, figsize=(12, 5))
- lengths = [row[0] for row in self.env.reward_stats] # type: ignore ## reward_stats exist
- rewards = [row[1] for row in self.env.reward_stats] # type: ignore ## reward_stats exist
+ lengths = [row[0] for row in self.env.reward_stats]
+ rewards = [row[1] for row in self.env.reward_stats]
# Episode rewards (win/loss performance)
axs[0].set_title("Episode rewards")
- reward_moving_average = get_moving_avgs(rewards, int(window / 10), "valid")
+ reward_moving_average = _get_moving_avgs(rewards, window // 10, "valid")
axs[0].plot(range(len(reward_moving_average)), reward_moving_average)
axs[0].set_ylabel("Average Reward")
axs[0].set_xlabel("Episode")
# Episode lengths (how many actions per hand)
axs[1].set_title("Episode lengths")
- length_moving_average = get_moving_avgs(lengths, int(window / 10), "valid")
+ length_moving_average = _get_moving_avgs(lengths, window // 10, "valid")
axs[1].plot(range(len(length_moving_average)), length_moving_average)
axs[1].set_ylabel("Average Episode Length")
axs[1].set_xlabel("Episode")
# Training error (how much we're still learning)
axs[2].set_title("Training Error")
- training_error_moving_average = get_moving_avgs(self.training_error, window, "same")
+ training_error_moving_average = _get_moving_avgs(self.training_error, window, "same")
axs[2].plot(range(len(training_error_moving_average)), training_error_moving_average)
axs[2].set_ylabel("Temporal Difference Error")
axs[2].set_xlabel("Step")
@@ -222,25 +328,27 @@ def get_moving_avgs(arr, window, convolution_mode):
plt.tight_layout()
plt.show()
- def analyse_episode(self, window: int = 100):
-
- def get_moving_avgs(arr, window, convolution_mode):
- """Compute moving average to smooth noisy data."""
- return np.convolve(np.array(arr).flatten(), np.ones(window), mode=convolution_mode) / window
+ def analyse_episode(self, window: int = 100) -> None:
+ """Plot moving averages of rewards and training error for one episode.
+ Parameters
+ ----------
+ window : int, optional
+ Number of steps used for the smoothing window (default 100).
+ """
# Smooth over the given episode window
- fig, axs = plt.subplots(ncols=2, figsize=(12, 5))
+ _, axs = plt.subplots(ncols=2, figsize=(12, 5))
# Episode rewards (win/loss performance)
axs[0].set_title("Episode rewards")
- reward_moving_average = get_moving_avgs(self.env.rewards, window, "valid") # type: ignore ## rewards exist
+ reward_moving_average = _get_moving_avgs(self.env.rewards, window, "valid")
axs[0].plot(range(len(reward_moving_average)), reward_moving_average)
axs[0].set_ylabel("Average Reward")
axs[0].set_xlabel("Episode")
# Training error (how much we're still learning)
axs[1].set_title("Training Error")
- training_error_moving_average = get_moving_avgs(self.training_error, window, "same")
+ training_error_moving_average = _get_moving_avgs(self.training_error, window, "same")
axs[1].plot(range(len(training_error_moving_average)), training_error_moving_average)
axs[1].set_ylabel("Temporal Difference Error")
axs[1].set_xlabel("Step")
@@ -248,22 +356,25 @@ def get_moving_avgs(arr, window, convolution_mode):
plt.tight_layout()
plt.show()
- def test_agent(self, num_episodes=1000):
+ def test_agent(self, num_episodes: int = 1000) -> str:
"""Test agent performance without learning or exploration."""
- total_rewards = []
+ total_rewards: list[float] = []
# Temporarily disable exploration for testing
old_epsilon = self.epsilon
self.epsilon = 0.0 # Pure exploitation
for _ in range(num_episodes):
- obs, info = self.env.reset()
+ obs, _ = self.env.reset()
+ assert isinstance(obs, tuple)
episode_reward = 0.0
done = False
while not done:
action = self.get_action(obs)
- obs, reward, terminated, truncated, info = self.env.step(action)
+ next_obs, reward, terminated, truncated, _ = self.env.step(action)
+ assert isinstance(next_obs, tuple)
+ obs = next_obs
episode_reward += float(reward)
done = terminated or truncated
diff --git a/src/crane_controller/reinforce_agent.py b/src/crane_controller/reinforce_agent.py
index 0a963ad..2448a17 100644
--- a/src/crane_controller/reinforce_agent.py
+++ b/src/crane_controller/reinforce_agent.py
@@ -1,7 +1,10 @@
+"""REINFORCE policy-gradient agent for continuous control."""
+
from __future__ import annotations
import logging
import random
+from typing import TYPE_CHECKING
import gymnasium as gym
import matplotlib.pyplot as plt
@@ -9,9 +12,12 @@
import pandas as pd
import seaborn as sns
import torch
-import torch.nn as nn
+from torch import nn
from torch.distributions.normal import Normal
+if TYPE_CHECKING:
+ from typing import SupportsFloat
+
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
@@ -19,15 +25,21 @@
class PolicyNetwork(nn.Module):
- """Parametrized Policy Network."""
+ """Parametrised policy network.
- def __init__(self, obs_space_dims: int, action_space_dims: int):
- """Initialize a neural network that estimates the mean and standard deviation
- of a normal distribution from which an action is sampled from.
+ Estimates the mean and standard deviation of a normal distribution
+ from which an action is sampled.
+ """
- Args:
- obs_space_dims: Dimension of the observation space
- action_space_dims: Dimension of the action space
+ def __init__(self, obs_space_dims: int, action_space_dims: int) -> None:
+ """Initialise the policy network.
+
+ Parameters
+ ----------
+ obs_space_dims : int
+ Dimension of the observation space.
+ action_space_dims : int
+ Dimension of the action space.
"""
super().__init__()
@@ -49,16 +61,22 @@ def __init__(self, obs_space_dims: int, action_space_dims: int):
self.policy_stddev_net = nn.Sequential(nn.Linear(hidden_space2, action_space_dims))
def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
- """Conditioned on the observation, returns the mean and standard deviation
- of a normal distribution from which an action is sampled.
+ """Return the mean and standard deviation of the action distribution.
+
+ Conditioned on the observation, produce parameters of a normal
+ distribution from which an action is sampled.
- Args:
- x: Observation from the environment
+ Parameters
+ ----------
+ x : torch.Tensor
+ Observation from the environment.
Returns
-------
- action_means: predicted mean of the normal distribution
- action_stddevs: predicted standard deviation of the normal distribution
+ action_means : torch.Tensor
+ Predicted mean of the normal distribution.
+ action_stddevs : torch.Tensor
+ Predicted standard deviation of the normal distribution.
"""
shared_features = self.shared_net(x.float())
@@ -69,30 +87,36 @@ def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
class REINFORCE:
- """REINFORCE algorithm."""
+ """REINFORCE policy-gradient algorithm."""
def __init__(
self,
- env: gym.Env,
+ env: gym.Env[object, object],
learning_rate: float = 1e-4,
gamma: float = 0.99,
eps: float = 1e-6,
- ):
- """Initialize an agent that learns a policy via REINFORCE algorithm [1]
- to solve the task at hand (Inverted Pendulum v4).
-
- Args:
- env (gym.Env): the environment to be trained
- learning_rate (float) = 1e-4: Learning rate for policy optimization
- gamma (float) = 0.99: Discount factor
- eps : float = 1e-6: small number for mathematical stability
+ ) -> None:
+ """Initialise an agent that learns a policy via REINFORCE.
+
+ Parameters
+ ----------
+ env : gym.Env[object, object]
+ The environment to be trained.
+ learning_rate : float, optional
+ Learning rate for policy optimisation (default 1e-4).
+ gamma : float, optional
+ Discount factor (default 0.99).
+ eps : float, optional
+ Small number for numerical stability (default 1e-6).
"""
self.env = env
assert self.env.observation_space.shape is not None
self.obs_space_dims = self.env.observation_space.shape[0] # Observation-space of environment
assert self.env.action_space.shape is not None
logger.error("Check the code for deciding whether an action space is discrete or continuous!")
- if 1 == len(self.env.action_space.shape): # discrete space
+ # TODO @aleksandarbabicdnv: The code executed in both branches of the if-else is exact same. Correct?
+ # ClaasRostock, 2026-04-23
+ if len(self.env.action_space.shape) == 1: # discrete space
self.action_space_dims = self.env.action_space.shape[0]
else: # continuous space
self.action_space_dims = self.env.action_space.shape[0] # Action-space of environment
@@ -102,25 +126,34 @@ def __init__(
self.gamma = gamma
self.eps = eps
- def reset(self):
+ self.probs: list[torch.Tensor] = []
+ self.rewards: list[SupportsFloat] = []
+ self.net: PolicyNetwork = PolicyNetwork(self.obs_space_dims, self.action_space_dims)
+ self.optimizer: torch.optim.AdamW = torch.optim.AdamW(self.net.parameters(), lr=self.learning_rate)
+
+ def reset(self) -> None:
+ """Reset episode state and reinitialize the policy network."""
self.probs = [] # Stores probability values of the sampled action
self.rewards = [] # Stores the corresponding rewards
self.net = PolicyNetwork(self.obs_space_dims, self.action_space_dims)
self.optimizer = torch.optim.AdamW(self.net.parameters(), lr=self.learning_rate)
- def sample_action(self, state: np.ndarray):
+ def sample_action(self, state: np.ndarray) -> int | np.ndarray:
"""Return an action, conditioned on the policy and observation.
- Args:
- state: Observation from the environment
+ Parameters
+ ----------
+ state : np.ndarray
+ Observation from the environment.
Returns
-------
- action: Action to be performed
+ int or np.ndarray
+ Discrete action index or continuous action array.
"""
- state = torch.from_numpy(state) # type: ignore ## according to documentation
- action_means, action_stddevs = self.net(state)
+ state_tensor = torch.from_numpy(state.astype(np.float32, copy=False))
+ action_means, action_stddevs = self.net(state_tensor)
# create a normal distribution from the predicted
# mean and standard deviation and sample an action
@@ -132,17 +165,16 @@ def sample_action(self, state: np.ndarray):
if self.env.action_space.shape is not None and not len(self.env.action_space.shape): # discrete space
return int(action[0])
- else:
- return action
+ return action
- def update(self):
- """Update the policy network's weights."""
- running_g = 0
+ def update(self) -> None:
+ """Update the policy network weights using collected episode data."""
+ running_g: float = 0.0
gs: list[float] = []
# Discounted return (backwards) - [::-1] will return an array in reverse
- for R in self.rewards[::-1]:
- running_g = R + self.gamma * running_g
+ for reward in self.rewards[::-1]:
+ running_g = float(reward) + self.gamma * running_g
gs.insert(0, running_g)
deltas = torch.tensor(gs)
@@ -155,41 +187,54 @@ def update(self):
# Update the policy network
self.optimizer.zero_grad()
- loss.backward()
- self.optimizer.step()
+ _ = loss.backward()
+ _ = self.optimizer.step()
# Empty / zero out all episode-centric/related variables
self.probs = []
self.rewards = []
- def do_training(self, num_episodes: int = 5000):
+ def do_training(self, num_episodes: int = 5000) -> list[list[float]]:
+ """Train the policy over multiple random seeds.
+
+ Runs training for each of the Fibonacci seeds ``(1, 2, 3, 5, 8)``.
+
+ Parameters
+ ----------
+ num_episodes : int, optional
+ Number of episodes per seed (default 5000).
+
+ Returns
+ -------
+ list[list[float]]
+ Reward history per episode for each seed.
+ """
wrapped_env = gym.wrappers.RecordEpisodeStatistics(self.env, 50) # Records episode-reward
- rewards_over_seeds = []
+ rewards_over_seeds: list[list[float]] = []
for seed in [1, 2, 3, 5, 8]: # Fibonacci seeds
# set seed
- torch.manual_seed(seed)
+ _ = torch.manual_seed(seed)
random.seed(seed)
- np.random.seed(seed)
# Reinitialize agent every seed
self.reset()
- reward_over_episodes = []
+ reward_over_episodes: list[float] = []
for episode in range(num_episodes):
# gymnasium v26 requires users to set seed while resetting the environment
- obs, info = wrapped_env.reset(seed=seed)
+ obs_arr, _ = wrapped_env.reset(seed=seed)
done = False
while not done:
- action = self.sample_action(obs)
+ action = self.sample_action(np.asarray(obs_arr))
# Step return type - `tuple[ObsType, SupportsFloat, bool, bool, dict[str, Any]]`
# These represent the next observation, the reward from the step,
# if the episode is terminated, if the episode is truncated and
# additional info from the step
- obs, reward, terminated, truncated, info = wrapped_env.step(action)
+ obs_arr, reward, terminated, truncated, _ = wrapped_env.step(action)
self.rewards.append(reward)
# End the episode when either truncated or terminated is true
@@ -203,13 +248,23 @@ def do_training(self, num_episodes: int = 5000):
if episode % 1000 == 0:
avg_reward = int(np.mean(wrapped_env.return_queue))
- print("Episode:", episode, "Average Reward:", avg_reward)
+ logger.info("Episode: %s Average Reward: %s", episode, avg_reward)
rewards_over_seeds.append(reward_over_episodes)
- def plot_learning_curve(self, rewards_over_seeds: list[float]):
+ return rewards_over_seeds
+
+ def plot_learning_curve(self, rewards_over_seeds: list[list[float]]) -> None:
+ """Plot the learning curve across all training seeds.
+
+ Parameters
+ ----------
+ rewards_over_seeds : list[list[float]]
+ Reward history per episode for each seed, as returned by
+ :meth:`do_training`.
+ """
df1 = pd.DataFrame(rewards_over_seeds).melt()
- df1.rename(columns={"variable": "episodes", "value": "reward"}, inplace=True)
+ df1 = df1.rename(columns={"variable": "episodes", "value": "reward"})
sns.set(style="darkgrid", context="talk", palette="rainbow")
- sns.lineplot(x="episodes", y="reward", data=df1).set(title=f"REINFORCE for {self.env.__name__}") # type: ignore
+ _ = sns.lineplot(x="episodes", y="reward", data=df1).set(title=f"REINFORCE for {type(self.env).__name__}")
plt.show()
diff --git a/src/crane_controller/wrappers/__init__.py b/src/crane_controller/wrappers/__init__.py
index 80e87be..996ff30 100644
--- a/src/crane_controller/wrappers/__init__.py
+++ b/src/crane_controller/wrappers/__init__.py
@@ -1,4 +1 @@
-# from crane_controller.wrappers.clip_reward import ClipReward
-# from crane_controller.wrappers.discrete_actions import DiscreteActions
-# from crane_controller.wrappers.reacher_weighted_reward import ReacherRewardWrapper
-# from crane_controller.wrappers.relative_position import RelativePosition
+"""Sub-package wrappers."""
diff --git a/src/crane_controller/wrappers/clip_reward.py b/src/crane_controller/wrappers/clip_reward.py
index 21118b5..a7e8617 100644
--- a/src/crane_controller/wrappers/clip_reward.py
+++ b/src/crane_controller/wrappers/clip_reward.py
@@ -1,14 +1,46 @@
+"""Gymnasium reward wrapper that clips rewards to a specified range."""
+
+import logging
+from typing import SupportsFloat
+
import gymnasium as gym
import numpy as np
+logger = logging.getLogger(__name__)
+
+
+class ClipReward(gym.RewardWrapper[object, object]):
+ """Gymnasium reward wrapper that clips rewards to a fixed range."""
-class ClipReward(gym.RewardWrapper):
- def __init__(self, env, min_reward, max_reward):
+ def __init__(self, env: gym.Env[object, object], min_reward: float, max_reward: float) -> None:
+ """Initialize the reward clipper.
+
+ Parameters
+ ----------
+ env : gym.Env[object, object]
+ The environment to wrap.
+ min_reward : float
+ Lower bound for clipped rewards.
+ max_reward : float
+ Upper bound for clipped rewards.
+ """
super().__init__(env)
self.min_reward = min_reward
self.max_reward = max_reward
self.reward_range = (min_reward, max_reward)
- def reward(self, reward):
- _reward = np.array([reward], float)
+ def reward(self, reward: SupportsFloat) -> np.ndarray:
+ """Clip the reward to the configured range.
+
+ Parameters
+ ----------
+ reward : SupportsFloat
+ Raw reward from the wrapped environment.
+
+ Returns
+ -------
+ np.ndarray
+ Single-element array with the clipped reward.
+ """
+ _reward = np.array([float(reward)], float)
return np.clip(_reward, self.min_reward, self.max_reward)
diff --git a/src/crane_controller/wrappers/discrete_actions.py b/src/crane_controller/wrappers/discrete_actions.py
index bb21962..8d3cec1 100644
--- a/src/crane_controller/wrappers/discrete_actions.py
+++ b/src/crane_controller/wrappers/discrete_actions.py
@@ -1,12 +1,38 @@
+"""Gymnasium action wrapper mapping discrete indices to continuous arrays."""
+
import gymnasium as gym
+import numpy as np
from gymnasium.spaces import Discrete
-class DiscreteActions(gym.ActionWrapper):
- def __init__(self, env, disc_to_cont):
- super().__init__(env)
+class DiscreteActions(gym.ActionWrapper[object, int, np.ndarray]):
+ """Gymnasium action wrapper mapping discrete indices to continuous arrays."""
+
+ def __init__(self, env: gym.Env[object, object], disc_to_cont: list[np.ndarray]) -> None:
+ """Initialize the discrete-to-continuous action wrapper.
+
+ Parameters
+ ----------
+ env : gym.Env[object, object]
+ The environment to wrap.
+ disc_to_cont : list[np.ndarray]
+ Lookup table mapping discrete action indices to continuous arrays.
+ """
+ super().__init__(env) # type: ignore[arg-type] # gymnasium Env type params are invariant
self.disc_to_cont = disc_to_cont
self.action_space = Discrete(len(disc_to_cont))
- def action(self, action):
+ def action(self, action: int) -> np.ndarray:
+ """Map a discrete action index to a continuous action array.
+
+ Parameters
+ ----------
+ action : int
+ Discrete action index.
+
+ Returns
+ -------
+ np.ndarray
+ Corresponding continuous action from the lookup table.
+ """
return self.disc_to_cont[action]
diff --git a/src/crane_controller/wrappers/reacher_weighted_reward.py b/src/crane_controller/wrappers/reacher_weighted_reward.py
index 7365023..3c56d99 100644
--- a/src/crane_controller/wrappers/reacher_weighted_reward.py
+++ b/src/crane_controller/wrappers/reacher_weighted_reward.py
@@ -1,13 +1,43 @@
+"""Gymnasium wrapper for weighted distance and control cost rewards."""
+
import gymnasium as gym
-class ReacherRewardWrapper(gym.Wrapper):
- def __init__(self, env, reward_dist_weight, reward_ctrl_weight):
+class ReacherRewardWrapper(gym.Wrapper[object, object, object, object]):
+ """Gymnasium wrapper combining distance and control cost rewards."""
+
+ def __init__(self, env: gym.Env[object, object], reward_dist_weight: float, reward_ctrl_weight: float) -> None:
+ """Initialize the weighted reward wrapper.
+
+ Parameters
+ ----------
+ env : gym.Env[object, object]
+ The environment to wrap.
+ reward_dist_weight : float
+ Weight applied to the distance reward component.
+ reward_ctrl_weight : float
+ Weight applied to the control cost reward component.
+ """
super().__init__(env)
self.reward_dist_weight = reward_dist_weight
self.reward_ctrl_weight = reward_ctrl_weight
- def step(self, action):
+ def step(self, action: object) -> tuple[object, float, bool, bool, dict[str, float]]:
+ """Step the environment and return a weighted reward.
+
+ Combines distance and control cost rewards from the info dict
+ using the configured weights.
+
+ Parameters
+ ----------
+ action : object
+ Action to perform.
+
+ Returns
+ -------
+ tuple[object, float, bool, bool, dict[str, float]]
+ ``(observation, reward, terminated, truncated, info)``.
+ """
obs, _, terminated, truncated, info = self.env.step(action)
reward = self.reward_dist_weight * info["reward_dist"] + self.reward_ctrl_weight * info["reward_ctrl"]
return obs, reward, terminated, truncated, info
diff --git a/src/crane_controller/wrappers/relative_position.py b/src/crane_controller/wrappers/relative_position.py
index beefd99..3047973 100644
--- a/src/crane_controller/wrappers/relative_position.py
+++ b/src/crane_controller/wrappers/relative_position.py
@@ -1,12 +1,35 @@
+"""Gymnasium observation wrapper converting positions to relative vectors."""
+
import gymnasium as gym
import numpy as np
from gymnasium.spaces import Box
-class RelativePosition(gym.ObservationWrapper):
- def __init__(self, env):
- super().__init__(env)
- self.observation_space = Box(shape=(2,), low=-np.inf, high=np.inf)
+class RelativePosition(gym.ObservationWrapper[dict[str, np.ndarray], object, np.ndarray]):
+ """Gymnasium observation wrapper converting positions to relative vectors."""
+
+ def __init__(self, env: gym.Env[object, object]) -> None:
+ """Initialize the relative position observation wrapper.
+
+ Parameters
+ ----------
+ env : gym.Env[object, object]
+ The environment to wrap.
+ """
+ super().__init__(env) # type: ignore[arg-type] # gymnasium Env type params are invariant
+ self.observation_space = Box(shape=(2,), low=-np.inf, high=np.inf) # type: ignore[assignment] # Box is compatible with Space
+
+ def observation(self, observation: dict[str, np.ndarray]) -> np.ndarray: # type: ignore[override] # transforms dict obs to ndarray
+ """Compute relative position from agent to target.
+
+ Parameters
+ ----------
+ observation : dict[str, np.ndarray]
+ Dictionary with ``"agent"`` and ``"target"`` position arrays.
- def observation(self, observation):
+ Returns
+ -------
+ np.ndarray
+ Relative position vector (target minus agent).
+ """
return observation["target"] - observation["agent"]
diff --git a/stubs/matplotlib-stubs/__init__.pyi b/stubs/matplotlib-stubs/__init__.pyi
new file mode 100644
index 0000000..6c92d1b
--- /dev/null
+++ b/stubs/matplotlib-stubs/__init__.pyi
@@ -0,0 +1,9 @@
+# Type stubs for matplotlib
+# Covers only the symbols used by this project.
+
+from . import _axes as _axes
+from . import axes as axes
+from . import figure as figure
+from . import lines as lines
+from . import pyplot as pyplot
+from . import text as text
diff --git a/stubs/matplotlib-stubs/_axes.pyi b/stubs/matplotlib-stubs/_axes.pyi
new file mode 100644
index 0000000..24ced52
--- /dev/null
+++ b/stubs/matplotlib-stubs/_axes.pyi
@@ -0,0 +1,16 @@
+# Type stubs for matplotlib.axes._axes (private submodule)
+# Pyright resolves seaborn's return type as matplotlib.axes._axes.Axes.
+
+from typing import Any
+
+from .lines import Line2D
+
+class Axes:
+ def plot(self, *args: Any, **kwargs: Any) -> list[Line2D]: ...
+ def set(self, **kwargs: Any) -> list[Any]: ...
+ def set_title(self, label: str, **kwargs: Any) -> Any: ...
+ def set_xlabel(self, xlabel: str, **kwargs: Any) -> Any: ...
+ def set_ylabel(self, ylabel: str, **kwargs: Any) -> Any: ...
+ def legend(self, *args: Any, **kwargs: Any) -> Any: ...
+ def set_xlim(self, *args: Any, **kwargs: Any) -> tuple[float, float]: ...
+ def set_ylim(self, *args: Any, **kwargs: Any) -> tuple[float, float]: ...
diff --git a/stubs/matplotlib-stubs/axes.pyi b/stubs/matplotlib-stubs/axes.pyi
new file mode 100644
index 0000000..1d231b8
--- /dev/null
+++ b/stubs/matplotlib-stubs/axes.pyi
@@ -0,0 +1,16 @@
+# Type stubs for matplotlib.axes
+# Covers only the symbols used by this project.
+
+from typing import Any
+
+from .lines import Line2D
+
+class Axes:
+ def plot(self, *args: Any, **kwargs: Any) -> list[Line2D]: ...
+ def set(self, **kwargs: Any) -> list[Any]: ...
+ def set_title(self, label: str, **kwargs: Any) -> Any: ...
+ def set_xlabel(self, xlabel: str, **kwargs: Any) -> Any: ...
+ def set_ylabel(self, ylabel: str, **kwargs: Any) -> Any: ...
+ def legend(self, *args: Any, **kwargs: Any) -> Any: ...
+ def set_xlim(self, *args: Any, **kwargs: Any) -> tuple[float, float]: ...
+ def set_ylim(self, *args: Any, **kwargs: Any) -> tuple[float, float]: ...
diff --git a/stubs/matplotlib-stubs/figure.pyi b/stubs/matplotlib-stubs/figure.pyi
new file mode 100644
index 0000000..108a26a
--- /dev/null
+++ b/stubs/matplotlib-stubs/figure.pyi
@@ -0,0 +1,4 @@
+# Type stubs for matplotlib.figure
+# Covers only the symbols used by this project.
+
+class Figure: ...
diff --git a/stubs/matplotlib-stubs/lines.pyi b/stubs/matplotlib-stubs/lines.pyi
new file mode 100644
index 0000000..341e163
--- /dev/null
+++ b/stubs/matplotlib-stubs/lines.pyi
@@ -0,0 +1,7 @@
+# Type stubs for matplotlib.lines
+# Covers only the symbols used by this project.
+
+from typing import Any
+
+class Line2D:
+ def set_data(self, *args: Any) -> None: ...
diff --git a/stubs/matplotlib-stubs/pyplot.pyi b/stubs/matplotlib-stubs/pyplot.pyi
new file mode 100644
index 0000000..4a793b4
--- /dev/null
+++ b/stubs/matplotlib-stubs/pyplot.pyi
@@ -0,0 +1,80 @@
+# Type stubs for matplotlib.pyplot
+# Covers only the symbols used by this project.
+
+from collections.abc import Sequence
+from typing import Any, Literal, overload
+
+from numpy.typing import NDArray
+
+from .axes import Axes
+from .figure import Figure
+from .lines import Line2D
+from .text import Text
+
+rcParams: dict[str, Any]
+
+@overload
+def subplots(
+ nrows: Literal[1] = ...,
+ ncols: Literal[1] = ...,
+ *,
+ sharex: bool | Literal["none", "all", "row", "col"] = ...,
+ sharey: bool | Literal["none", "all", "row", "col"] = ...,
+ squeeze: Literal[True] = ...,
+ width_ratios: Sequence[float] | None = ...,
+ height_ratios: Sequence[float] | None = ...,
+ subplot_kw: dict[str, Any] | None = ...,
+ gridspec_kw: dict[str, Any] | None = ...,
+ **fig_kw: Any,
+) -> tuple[Figure, Axes]: ...
+@overload
+def subplots(
+ nrows: int = ...,
+ ncols: int = ...,
+ *,
+ sharex: bool | Literal["none", "all", "row", "col"] = ...,
+ sharey: bool | Literal["none", "all", "row", "col"] = ...,
+ squeeze: Literal[False],
+ width_ratios: Sequence[float] | None = ...,
+ height_ratios: Sequence[float] | None = ...,
+ subplot_kw: dict[str, Any] | None = ...,
+ gridspec_kw: dict[str, Any] | None = ...,
+ **fig_kw: Any,
+) -> tuple[Figure, NDArray[Any]]: ...
+@overload
+def subplots(
+ nrows: int = ...,
+ ncols: int = ...,
+ *,
+ sharex: bool | Literal["none", "all", "row", "col"] = ...,
+ sharey: bool | Literal["none", "all", "row", "col"] = ...,
+ squeeze: bool = ...,
+ width_ratios: Sequence[float] | None = ...,
+ height_ratios: Sequence[float] | None = ...,
+ subplot_kw: dict[str, Any] | None = ...,
+ gridspec_kw: dict[str, Any] | None = ...,
+ **fig_kw: Any,
+) -> tuple[Figure, Any]: ...
+def show(*, block: bool | None = None) -> None: ...
+def ion() -> None: ...
+def title(
+ label: str,
+ fontdict: dict[str, Any] | None = None,
+ loc: Literal["left", "center", "right"] | None = None,
+ pad: float | None = None,
+ *,
+ y: float | None = None,
+ **kwargs: Any,
+) -> Text: ...
+def plot(
+ *args: Any,
+ scalex: bool = True,
+ scaley: bool = True,
+ data: Any = None,
+ **kwargs: Any,
+) -> list[Line2D]: ...
+def xlim(*args: Any, **kwargs: Any) -> tuple[float, float]: ...
+def ylim(*args: Any, **kwargs: Any) -> tuple[float, float]: ...
+def tight_layout(**kwargs: Any) -> None: ...
+def pause(interval: float) -> None: ...
+def legend(*args: Any, **kwargs: Any) -> Any: ...
diff --git a/stubs/matplotlib-stubs/text.pyi b/stubs/matplotlib-stubs/text.pyi
new file mode 100644
index 0000000..4aff446
--- /dev/null
+++ b/stubs/matplotlib-stubs/text.pyi
@@ -0,0 +1,4 @@
+# Type stubs for matplotlib.text
+# Covers only the symbols used by this project.
+
+class Text: ...
diff --git a/stubs/stable_baselines3-stubs/__init__.pyi b/stubs/stable_baselines3-stubs/__init__.pyi
new file mode 100644
index 0000000..299d732
--- /dev/null
+++ b/stubs/stable_baselines3-stubs/__init__.pyi
@@ -0,0 +1,4 @@
+# Type stubs for stable_baselines3
+# Covers only the symbols used by this project.
+
+from .ppo import PPO as PPO
diff --git a/stubs/stable_baselines3-stubs/common/__init__.pyi b/stubs/stable_baselines3-stubs/common/__init__.pyi
new file mode 100644
index 0000000..754f249
--- /dev/null
+++ b/stubs/stable_baselines3-stubs/common/__init__.pyi
@@ -0,0 +1 @@
+# Type stubs for stable_baselines3.common
diff --git a/stubs/stable_baselines3-stubs/common/callbacks.pyi b/stubs/stable_baselines3-stubs/common/callbacks.pyi
new file mode 100644
index 0000000..3b9076f
--- /dev/null
+++ b/stubs/stable_baselines3-stubs/common/callbacks.pyi
@@ -0,0 +1,3 @@
+# Type stubs for stable_baselines3.common.callbacks
+
+class BaseCallback: ...
diff --git a/stubs/stable_baselines3-stubs/common/env_util.pyi b/stubs/stable_baselines3-stubs/common/env_util.pyi
new file mode 100644
index 0000000..68fbbce
--- /dev/null
+++ b/stubs/stable_baselines3-stubs/common/env_util.pyi
@@ -0,0 +1,21 @@
+# Type stubs for stable_baselines3.common.env_util
+
+from collections.abc import Callable
+from typing import Any
+
+from gymnasium import Env
+from stable_baselines3.common.vec_env import VecEnv
+
+def make_vec_env(
+ env_id: str | type[Env[Any, Any]] | Callable[..., Env[Any, Any]],
+ n_envs: int = 1,
+ seed: int | None = None,
+ start_index: int = 0,
+ monitor_dir: str | None = None,
+ wrapper_class: type[Any] | None = None,
+ env_kwargs: dict[str, Any] | None = None,
+ vec_env_cls: type[VecEnv] | None = None,
+ vec_env_kwargs: dict[str, Any] | None = None,
+ monitor_kwargs: dict[str, Any] | None = None,
+ wrapper_kwargs: dict[str, Any] | None = None,
+) -> VecEnv: ...
diff --git a/stubs/stable_baselines3-stubs/common/evaluation.pyi b/stubs/stable_baselines3-stubs/common/evaluation.pyi
new file mode 100644
index 0000000..af3fe26
--- /dev/null
+++ b/stubs/stable_baselines3-stubs/common/evaluation.pyi
@@ -0,0 +1,18 @@
+# Type stubs for stable_baselines3.common.evaluation
+
+from typing import Any
+
+from gymnasium import Env
+from stable_baselines3.common.vec_env import VecEnv
+
+def evaluate_policy(
+ model: Any,
+ env: Env[Any, Any] | VecEnv,
+ n_eval_episodes: int = 10,
+ deterministic: bool = True,
+ render: bool = False,
+ callback: Any = None,
+ reward_threshold: float | None = None,
+ return_episode_rewards: bool = False,
+ warn: bool = True,
+) -> tuple[float, float]: ...
diff --git a/stubs/stable_baselines3-stubs/common/vec_env.pyi b/stubs/stable_baselines3-stubs/common/vec_env.pyi
new file mode 100644
index 0000000..0785ad6
--- /dev/null
+++ b/stubs/stable_baselines3-stubs/common/vec_env.pyi
@@ -0,0 +1,47 @@
+# Type stubs for stable_baselines3.common.vec_env
+# Covers only the symbols used by this project.
+
+from typing import Any
+
+import numpy as np
+from gymnasium import Env
+
+class VecEnv:
+ num_envs: int
+
+ def reset(self) -> np.ndarray: ...
+ def step(self, actions: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray, list[dict[str, Any]]]: ...
+ def render(self, mode: str = "human") -> None: ...
+ def close(self) -> None: ...
+ def seed(self, seed: int | None = None) -> list[int | None]: ...
+
+class DummyVecEnv(VecEnv):
+ envs: list[Env[Any, Any]]
+
+class VecEnvWrapper(VecEnv):
+ venv: VecEnv
+
+ def __init__(self, venv: VecEnv, **kwargs: Any) -> None: ...
+
+class RunningMeanStd:
+ mean: np.ndarray
+ var: np.ndarray
+ count: float
+
+class VecNormalize(VecEnvWrapper):
+ training: bool
+ norm_reward: bool
+ obs_rms: RunningMeanStd
+
+ def __init__(
+ self,
+ venv: VecEnv,
+ training: bool = True,
+ norm_obs: bool = True,
+ norm_reward: bool = True,
+ **kwargs: Any,
+ ) -> None: ...
+ @staticmethod
+ def load(load_path: str, venv: VecEnv) -> VecNormalize: ...
+ def save(self, save_path: str) -> None: ...
+ def normalize_obs(self, obs: np.ndarray | dict[str, np.ndarray]) -> np.ndarray | dict[str, np.ndarray]: ...
diff --git a/stubs/stable_baselines3-stubs/ppo.pyi b/stubs/stable_baselines3-stubs/ppo.pyi
new file mode 100644
index 0000000..e5075fa
--- /dev/null
+++ b/stubs/stable_baselines3-stubs/ppo.pyi
@@ -0,0 +1,44 @@
+# Type stubs for stable_baselines3.ppo
+# Covers only the symbols used by this project.
+
+from io import BufferedIOBase
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from gymnasium import Env
+from numpy.typing import NDArray
+from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.common.vec_env import VecEnv
+
+class PPO:
+ def __init__(self, policy: str, env: Env[Any, Any] | VecEnv, *, verbose: int = 0, **kwargs: Any) -> None: ...
+ @classmethod
+ def load(
+ cls,
+ path: str | Path | BufferedIOBase,
+ env: Env[Any, Any] | VecEnv | None = None,
+ device: torch.device | str = "auto",
+ custom_objects: dict[str, Any] | None = None,
+ print_system_info: bool = False,
+ force_reset: bool = True,
+ **kwargs: Any,
+ ) -> PPO: ...
+ def learn(
+ self,
+ total_timesteps: int,
+ callback: BaseCallback | list[BaseCallback] | None = None,
+ log_interval: int = 1,
+ tb_log_name: str = "PPO",
+ reset_num_timesteps: bool = True,
+ progress_bar: bool = False,
+ ) -> PPO: ...
+ def save(self, path: str | Path) -> None: ...
+ def predict(
+ self,
+ observation: NDArray[np.floating[Any]],
+ state: NDArray[np.floating[Any]] | None = None,
+ episode_start: NDArray[np.bool_] | None = None,
+ deterministic: bool = False,
+ ) -> tuple[NDArray[np.floating[Any]], NDArray[np.floating[Any]] | None]: ...
diff --git a/stubs/torch-stubs/__init__.pyi b/stubs/torch-stubs/__init__.pyi
new file mode 100644
index 0000000..f7681b9
--- /dev/null
+++ b/stubs/torch-stubs/__init__.pyi
@@ -0,0 +1,66 @@
+# Type stubs for torch 2.10.0
+# Covers only the symbols used by this project.
+
+from collections.abc import Iterable, Sequence
+from typing import Any, Self, overload
+
+import numpy as np
+from torch import cuda as cuda
+from torch import distributions as distributions
+from torch import nn as nn
+from torch import optim as optim
+
+class device:
+ def __init__(self, type: str, index: int | None = None) -> None: ...
+
+class Generator: ...
+
+class Size(tuple[int, ...]):
+ def __new__(cls, iterable: Iterable[int] = ...) -> Self: ...
+
+class Tensor:
+ # Casting / conversion
+ def float(self) -> Tensor: ...
+ def numpy(self, *, force: bool = False) -> np.ndarray[Any, Any]: ...
+ def item(self) -> int | float: ...
+
+ # Shape manipulation
+ def squeeze(self, dim: int | None = None) -> Tensor: ...
+
+ # Autograd
+ def backward(
+ self,
+ gradient: Tensor | None = None,
+ retain_graph: bool | None = None,
+ create_graph: bool = False,
+ inputs: Sequence[Tensor] | None = None,
+ ) -> None: ...
+
+ # Indexing
+ def __getitem__(self, index: Any) -> Tensor: ...
+
+ # Arithmetic
+ def __add__(self, other: Tensor | int | float) -> Tensor: ...
+ def __radd__(self, other: Tensor | int | float) -> Tensor: ...
+ def __sub__(self, other: Tensor | int | float) -> Tensor: ...
+ def __rsub__(self, other: Tensor | int | float) -> Tensor: ...
+ def __mul__(self, other: Tensor | int | float) -> Tensor: ...
+ def __rmul__(self, other: Tensor | int | float) -> Tensor: ...
+ def __neg__(self) -> Tensor: ...
+ def __truediv__(self, other: Tensor | int | float) -> Tensor: ...
+
+# --- Module-level functions ---
+
+def from_numpy(ndarray: np.ndarray[Any, Any]) -> Tensor: ...
+def manual_seed(seed: int) -> Generator: ...
+def tensor(
+ data: Any, *, dtype: Any = None, device: device | str | None = None, requires_grad: bool = False
+) -> Tensor: ...
+def log(input: Tensor) -> Tensor: ...
+def exp(input: Tensor) -> Tensor: ...
+def stack(tensors: Sequence[Tensor], dim: int = 0) -> Tensor: ...
+@overload
+def sum(input: Tensor) -> Tensor: ...
+@overload
+def sum(input: Tensor, dim: int | tuple[int, ...], keepdim: bool = False) -> Tensor: ...
+def set_default_device(device: device | str | None) -> None: ...
diff --git a/stubs/torch-stubs/cuda/__init__.pyi b/stubs/torch-stubs/cuda/__init__.pyi
new file mode 100644
index 0000000..1589e37
--- /dev/null
+++ b/stubs/torch-stubs/cuda/__init__.pyi
@@ -0,0 +1,4 @@
+# Type stubs for torch.cuda
+# Covers only the symbols used by this project.
+
+def is_available() -> bool: ...
diff --git a/stubs/torch-stubs/distributions/__init__.pyi b/stubs/torch-stubs/distributions/__init__.pyi
new file mode 100644
index 0000000..4fc67b1
--- /dev/null
+++ b/stubs/torch-stubs/distributions/__init__.pyi
@@ -0,0 +1,2 @@
+# Type stubs for torch.distributions
+from torch import normal as normal
diff --git a/stubs/torch-stubs/distributions/normal.pyi b/stubs/torch-stubs/distributions/normal.pyi
new file mode 100644
index 0000000..e1e3db6
--- /dev/null
+++ b/stubs/torch-stubs/distributions/normal.pyi
@@ -0,0 +1,16 @@
+# Type stubs for torch.distributions.normal
+# Covers only the symbols used by this project.
+
+from torch import Tensor
+
+class Normal:
+ loc: Tensor | float
+ scale: Tensor | float
+ def __init__(
+ self,
+ loc: Tensor | float,
+ scale: Tensor | float,
+ validate_args: bool | None = None,
+ ) -> None: ...
+ def sample(self, sample_shape: tuple[int, ...] = ()) -> Tensor: ...
+ def log_prob(self, value: Tensor) -> Tensor: ...
diff --git a/stubs/torch-stubs/nn/__init__.pyi b/stubs/torch-stubs/nn/__init__.pyi
new file mode 100644
index 0000000..6828270
--- /dev/null
+++ b/stubs/torch-stubs/nn/__init__.pyi
@@ -0,0 +1,26 @@
+# Type stubs for torch.nn
+# Covers only the symbols used by this project.
+
+from collections.abc import Iterator
+from typing import Any
+
+from torch import Tensor
+
+class Module:
+ def __init__(self) -> None: ...
+ def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
+ def parameters(self, recurse: bool = True) -> Iterator[Tensor]: ...
+ def forward(self, *args: Any, **kwargs: Any) -> Any: ...
+
+class Sequential(Module):
+ def __init__(self, *args: Module) -> None: ...
+ def forward(self, input: Tensor) -> Tensor: ...
+
+class Linear(Module):
+ in_features: int
+ out_features: int
+ def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None: ...
+ def forward(self, input: Tensor) -> Tensor: ...
+
+class Tanh(Module):
+ def forward(self, input: Tensor) -> Tensor: ...
diff --git a/stubs/torch-stubs/optim/__init__.pyi b/stubs/torch-stubs/optim/__init__.pyi
new file mode 100644
index 0000000..7584292
--- /dev/null
+++ b/stubs/torch-stubs/optim/__init__.pyi
@@ -0,0 +1,28 @@
+# Type stubs for torch.optim
+# Covers only the symbols used by this project.
+
+from collections.abc import Callable, Iterable
+from typing import Any
+
+from torch import Tensor
+
+class Optimizer:
+ def zero_grad(self, set_to_none: bool = True) -> None: ...
+ def step(self, closure: Callable[[], Tensor] | None = None) -> Tensor | None: ...
+
+class AdamW(Optimizer):
+ def __init__(
+ self,
+ params: Iterable[Tensor] | Iterable[dict[str, Any]],
+ lr: float = 1e-3,
+ betas: tuple[float, float] = (0.9, 0.999),
+ eps: float = 1e-8,
+ weight_decay: float = 0.01,
+ amsgrad: bool = False,
+ *,
+ maximize: bool = False,
+ foreach: bool | None = None,
+ capturable: bool = False,
+ differentiable: bool = False,
+ fused: bool | None = None,
+ ) -> None: ...
diff --git a/tests/.gitignore b/tests/.gitignore
new file mode 100644
index 0000000..fe195b0
--- /dev/null
+++ b/tests/.gitignore
@@ -0,0 +1,2 @@
+temp_*
+~$temp*
\ No newline at end of file
diff --git a/tests/conftest.py b/tests/conftest.py
index 5d728ae..580407c 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,20 +1,42 @@
+"""Test configuration and fixtures."""
+
import logging
import os
+from collections.abc import Callable
from pathlib import Path
from shutil import rmtree
import pytest
+import torch
+import torch.cuda
+from py_crane.crane import Crane
from crane_controller.crane_factory import build_crane
+CUDA_AVAILABLE: bool = torch.cuda.is_available()
+
+TORCH_DEVICES: list[str] = ["cuda", "cpu"] if CUDA_AVAILABLE else ["cpu"]
+
+
+@pytest.fixture(scope="class", params=TORCH_DEVICES)
+def vary_torch_default_device(request: pytest.FixtureRequest):
+ torch.set_default_device(request.param)
+ yield
+ torch.set_default_device("cpu") # reset to default device after test
+
@pytest.fixture(scope="package", autouse=True)
-def chdir() -> None:
+def chdir():
"""
Fixture that changes the current working directory to the 'test_working_directory' folder.
This fixture is automatically used for the entire package.
"""
+ original_cwd = Path.cwd()
os.chdir(Path(__file__).parent.absolute() / "test_working_directory")
+ try:
+ yield
+ finally:
+ os.chdir(original_cwd) # reset to original working directory after tests
@pytest.fixture(scope="package", autouse=True)
@@ -26,11 +48,11 @@ def test_dir() -> Path:
return Path(__file__).parent.absolute()
-output_dirs = [
+output_dirs: list[str] = [
"results",
"data",
]
-output_files = [
+output_files: list[str] = [
"*test*.pdf",
]
@@ -55,7 +77,8 @@ def _remove_output_dirs_and_files() -> None:
for pattern in output_files:
for file in Path.cwd().glob(pattern):
_file = Path(file)
- _file.unlink(missing_ok=True)
+ if _file.is_file():
+ _file.unlink(missing_ok=True)
@pytest.fixture(autouse=True)
@@ -75,14 +98,36 @@ def logger() -> logging.Logger:
@pytest.fixture
-def crane():
+def crane() -> Callable[..., Crane]:
return build_crane
-def pytest_addoption(parser: pytest.Parser):
- parser.addoption("--show", action="store", default=False)
+def pytest_addoption(parser: pytest.Parser) -> None:
+ parser.addoption(
+ "--show",
+ action="store_true",
+ default=False,
+ help=(
+ "Command line switch to show plots during tests, and dump additional results to console. By default, False."
+ ),
+ )
@pytest.fixture(scope="session")
-def show(request: pytest.FixtureRequest):
- return request.config.getoption("--show")
+def show(request: pytest.FixtureRequest) -> bool:
+ return bool(request.config.getoption("--show"))
+
+
+@pytest.fixture
+def v0() -> float:
+ return 1.0
+
+
+@pytest.fixture
+def reward_limit() -> float:
+ return 0.0
+
+
+@pytest.fixture
+def trained() -> tuple[str, bool]:
+ return ("anti-pendulum.json", False)
diff --git a/tests/test_algorithm.py b/tests/test_algorithm.py
index 4074341..be54126 100644
--- a/tests/test_algorithm.py
+++ b/tests/test_algorithm.py
@@ -1,5 +1,8 @@
import logging
-from typing import Callable
+from collections.abc import Callable
+
+import pytest
+from py_crane.crane import Crane
from crane_controller.algorithm import AlgorithmAgent
from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
@@ -8,24 +11,24 @@
logger = logging.getLogger(__name__)
+@pytest.mark.slow
def test_algorithm_strategies(
- crane: Callable,
+ crane: Callable[..., Crane],
+ *,
show: bool,
- reward_limit: float = 1000.0,
- start_speed: float = 0.0,
-):
+) -> None:
env = AntiPendulumEnv(
crane,
- start_speed=start_speed,
+ start_speed=0.0,
render_mode="plot" if show else "none",
- reward_limit=reward_limit,
+ reward_limit=1000.0,
discrete=QLearningAgent.DEFAULT_DISCRETE.copy(),
)
agent = AlgorithmAgent(env)
agent.do_strategies(max_steps=5000 if show else 10)
-def test_algorithm(crane: Callable, show: bool):
+def test_algorithm(crane: Callable[..., Crane], *, show: bool) -> None:
env = AntiPendulumEnv(
crane,
start_speed=0.0,
@@ -54,7 +57,7 @@ def test_algorithm(crane: Callable, show: bool):
import os
from pathlib import Path
- import pytest # noqa: F401
+ import pytest
from crane_controller.crane_factory import build_crane # noqa: F401
diff --git a/tests/test_environment.py b/tests/test_environment.py
index 2538a03..784915d 100644
--- a/tests/test_environment.py
+++ b/tests/test_environment.py
@@ -1,6 +1,6 @@
import logging
from collections import defaultdict
-from typing import Callable, Generator
+from collections.abc import Callable, Generator
import matplotlib.pyplot as plt
import numpy as np
@@ -20,36 +20,38 @@ def show_figure(
title: str = "",
):
"""Plot selected traces."""
- fig, (ax1, ax2) = plt.subplots(1, 2)
+ _, (ax1, ax2) = plt.subplots(1, 2)
for label, trace in traces.items():
- if selection is None:
+ if (selection is not None and label in selection and selection[label] == 1) or selection is None:
_ = ax1.plot(times, trace, label=label)
- else:
- if label in selection:
- if selection[label] == 1:
- _ = ax1.plot(times, trace, label=label)
- elif selection[label] == 2:
- _ = ax2.plot(times, trace, label=label)
+ elif label in selection and selection[label] == 2:
+ _ = ax2.plot(times, trace, label=label)
_ = ax1.legend()
_ = ax2.legend()
- plt.title(title)
+ _ = plt.title(title)
plt.show()
def movement(crane: Crane, dt: float = 0.01, t_end: float = 10.0) -> Generator[tuple[float, Crane], None, None]:
"""Step through time yielding (time, crane) tuples with alternating acceleration."""
- f, p, w = list(crane.booms())
+ _, _, _ = list(crane.booms())
acc = -0.1
crane.velocity = np.array((acc, 0.0, 0.0), float)
for time in np.linspace(0.0, t_end, int(t_end / dt) + 1):
if abs(time - int(time)) < 1e-6:
acc = -acc
crane.d_velocity[0] = acc
- crane.do_step(time, dt)
+ _ = crane.do_step(time, dt)
yield (time + dt, crane)
-def test_environment(crane: Callable, show: bool, v0: float = 1.0, reward_limit=0.0):
+def test_environment(
+ crane: Callable[..., Crane],
+ *,
+ show: bool,
+ v0: float,
+ reward_limit: float,
+) -> None:
env = AntiPendulumEnv(
crane,
start_speed=v0,
@@ -57,16 +59,16 @@ def test_environment(crane: Callable, show: bool, v0: float = 1.0, reward_limit=
reward_limit=reward_limit,
discrete=QLearningAgent.DEFAULT_DISCRETE.copy(),
)
- assert env.action_space.n == 3 # type: ignore[attr-defined]
- assert env.action_space.start == 0 # type: ignore[attr-defined]
+ assert env.action_space.n == 3 # type: ignore[attr-defined,unused-ignore]
+ assert env.action_space.start == 0 # type: ignore[attr-defined,unused-ignore]
assert env.action_space.dtype == np.int64
assert isinstance(env.action_space.seed(), int)
- assert len(env.observation_space.nvec) == 5 # type: ignore[attr-defined]
- assert np.allclose(env.observation_space.nvec, [7, 2, 2, 6, 2]) # type: ignore[attr-defined]
- assert np.allclose(env.observation_space.start, [0, 0, 0, 0, 0]) # type: ignore[attr-defined]
+ assert len(env.observation_space.nvec) == 5 # type: ignore[attr-defined,unused-ignore]
+ assert np.allclose(env.observation_space.nvec, [7, 2, 2, 6, 2]) # type: ignore[attr-defined,unused-ignore]
+ assert np.allclose(env.observation_space.start, [0, 0, 0, 0, 0]) # type: ignore[attr-defined,unused-ignore]
assert env.observation_space.dtype == np.int64
assert isinstance(env.observation_space.seed(), int)
- q_values = defaultdict(lambda: np.array([env.low_reward()] * env.action_space.n)) # type: ignore
+ q_values = defaultdict(lambda: np.array([env.low_reward()] * env.action_space.n)) # type: ignore[var-annotated,attr-defined]
obs1 = np.array((0, 1, 1, 3, 0), int)
obs2 = np.array((4, 0, 0, 1, 1), int)
q_values[obs1.tobytes()]
@@ -75,54 +77,42 @@ def test_environment(crane: Callable, show: bool, v0: float = 1.0, reward_limit=
assert q_values[obs2.tobytes()][2] == -98.1
-def test_observation_space_dtype(crane: Callable):
- env = AntiPendulumEnv(crane)
- assert env.observation_space.dtype == np.float64
-
-
-def test_observations_are_float(crane: Callable):
- env = AntiPendulumEnv(crane)
- env.reset()
- obs, _, _, _, _ = env.step(1) # one physics step produces fractional values
- assert obs.dtype == np.float64
- assert not np.all(obs == obs.astype(int)) # sub-integer precision is preserved
-
-
-def test_init(crane: Crane, show: bool = False):
+def test_init(crane: Callable[..., Crane], *, show: bool) -> None:
"""Test the initialization of the environment."""
- env = AntiPendulumEnv(crane, seed=1, start_speed=-1.0, render_mode="play-back" if show else "data")
+ env = AntiPendulumEnv(crane, seed=1, start_speed=1.0, render_mode="play-back" if show else "data")
rnd_u = env.np_random.uniform(2, 8)
rnd_r = env.np_random.random()
assert rnd_u == 5.07092974820154, f"Returns pseudo-random numbers when seed is given. Got {rnd_u} for seed 1"
assert rnd_r == 0.9504636963259353, f"Returns pseudo-random numbers when seed is given. Got {rnd_r} for seed 1"
- obs, inf = env.reset()
- assert np.allclose(obs, [0.0, 0.0, 0.0, -0.7405126971046593]), f"Found {obs}"
+ obs, inf = env.reset(seed=1)
+ assert np.allclose(obs, [0.0, 0.0, np.pi, 0.017453292519943295]), f"Found {obs[3]}"
assert inf["steps"] == 0
- assert abs(inf["reward"] + 0.5 * (-0.7405126971046593) ** 2) < 1e-9, f"Found initial reward {inf['reward']}"
- obs, reward, terminated, truncated, info = env.step(0)
- assert obs[0] == -1e-5, f"Found {obs[0]}"
- assert obs[1] == -0.001, f"Found {obs[1]}"
+ assert abs(inf["reward"] + 0.5 * 0.017453292519943295**2) < 1e-9, f"Found initial reward {inf['reward']}"
+ obs, reward, terminated, truncated, _ = env.step(-1)
+ assert obs[0] == -0.1
+ assert obs[1] == -0.1
assert not terminated
assert not truncated
- rewards = []
+ rewards: list[float] = []
for _ in range(100):
- obs, reward, terminated, truncated, info = env.step(env.np_random.integers(0, 3))
+ obs, reward, terminated, truncated, _ = env.step(int(env.np_random.integers(-1, 2)))
rewards.append(reward)
if show:
show_figure(times=np.linspace(0, 100, 100), traces={"rewards": rewards})
- env.reset()
-
+ _ = env.reset()
-if __name__ == "__main__":
- import os
- from pathlib import Path
- import pytest # noqa: F401
+def test_observation_space_dtype(crane: Callable[..., Crane]) -> None:
+ """Test that the continuous observation space uses float64 dtype."""
+ env = AntiPendulumEnv(crane)
+ assert env.observation_space.dtype == np.float64
- from crane_controller.crane_factory import build_crane # noqa: F401
- retcode = pytest.main(["-rP -s -v", "--show", "False", __file__])
- assert retcode == 0, f"Return code {retcode}"
- os.chdir(Path(__file__).parent.absolute() / "test_working_directory")
- # test_init(build_crane, show=True)
- # test_environment(build_crane, show=True)
+def test_observations_are_float(crane: Callable[..., Crane]) -> None:
+ """Test that observations preserve sub-integer precision after a physics step."""
+ env = AntiPendulumEnv(crane)
+ _ = env.reset()
+ obs, _, _, _, _ = env.step(1) # one physics step produces fractional values
+ assert isinstance(obs, np.ndarray)
+ assert obs.dtype == np.float64
+ assert not np.all(obs == obs.astype(int)) # sub-integer precision is preserved
diff --git a/tests/test_ppo.py b/tests/test_ppo.py
index dfdd08e..025e003 100644
--- a/tests/test_ppo.py
+++ b/tests/test_ppo.py
@@ -1,8 +1,10 @@
import logging
+from collections.abc import Callable
from pathlib import Path
import numpy as np
from py_crane.crane import Crane
+from stable_baselines3.common.running_mean_std import RunningMeanStd
from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
from crane_controller.ppo_agent import ProximalPolicyOptimizationAgent
@@ -10,9 +12,9 @@
logger = logging.getLogger(__name__)
-def test_monitor(crane: Crane, show: bool):
+def test_monitor(crane: Callable[..., Crane], *, show: bool) -> None:
agent = ProximalPolicyOptimizationAgent(
- AntiPendulumEnv, # type: ignore[arg-type]
+ AntiPendulumEnv,
n_envs=1,
env_kwargs={
"crane": crane,
@@ -24,10 +26,11 @@ def test_monitor(crane: Crane, show: bool):
agent.do_training(1000)
-def test_ppo_saves_vecnorm(crane, tmp_path):
+def test_ppo_saves_vecnorm(crane: Callable[..., Crane], tmp_path: Path) -> None:
+ """Test that do_training saves the VecNormalize statistics alongside the model."""
save_path = str(tmp_path / "model.zip")
agent = ProximalPolicyOptimizationAgent(
- AntiPendulumEnv, # type: ignore[arg-type]
+ AntiPendulumEnv,
n_envs=1,
env_kwargs={"crane": crane, "start_speed": 1.0},
trained=(save_path, True),
@@ -36,24 +39,13 @@ def test_ppo_saves_vecnorm(crane, tmp_path):
assert (tmp_path / "model_vecnorm.pkl").exists()
-def test_ppo_vecnorm_updates(crane):
+def test_ppo_vecnorm_updates(crane: Callable[..., Crane]) -> None:
+ """Test that the VecNormalize running mean is updated during training."""
agent = ProximalPolicyOptimizationAgent(
- AntiPendulumEnv, # type: ignore[arg-type]
+ AntiPendulumEnv,
n_envs=1,
env_kwargs={"crane": crane, "start_speed": 1.0},
)
agent.do_training(500, progress_bar=False)
+ assert isinstance(agent.vec_env.obs_rms, RunningMeanStd)
assert not np.allclose(agent.vec_env.obs_rms.mean, 0.0)
-
-
-if __name__ == "__main__":
- import os
-
- import pytest # noqa: F401
-
- from crane_controller.crane_factory import build_crane # noqa: F401
-
- retcode = pytest.main(["-rP -s -v", "--show", "False", __file__])
- assert retcode == 0, f"Return code {retcode}"
- os.chdir(Path(__file__).parent.absolute() / "test_working_directory")
- # test_monitor(build_crane, show=True)
diff --git a/tests/test_q.py b/tests/test_q.py
index a86648f..705ce1a 100644
--- a/tests/test_q.py
+++ b/tests/test_q.py
@@ -1,7 +1,8 @@
import logging
-from typing import Callable
+from collections.abc import Callable
import numpy as np
+from py_crane.crane import Crane
from crane_controller.envs.controlled_crane_pendulum import AntiPendulumEnv
from crane_controller.q_agent import QLearningAgent
@@ -9,7 +10,7 @@
logger = logging.getLogger(__name__)
-def test_smoke(crane: Callable, show: bool):
+def test_smoke(crane: Callable[..., Crane], *, show: bool) -> None:
env = AntiPendulumEnv(
crane,
start_speed=-1.0,
@@ -21,7 +22,7 @@ def test_smoke(crane: Callable, show: bool):
agent.do_episodes(n_episodes=5, max_steps=200)
-def test_q_analyse(crane: Callable, trained: tuple[str, bool] = ("anti-pendulum.json", False)):
+def test_q_analyse(crane: Callable[..., Crane], *, trained: tuple[str, bool]) -> None:
env = AntiPendulumEnv(
crane,
discrete=QLearningAgent.DEFAULT_DISCRETE.copy(),
@@ -31,25 +32,10 @@ def test_q_analyse(crane: Callable, trained: tuple[str, bool] = ("anti-pendulum.
assert len(k) == 5, len(v) == 3
for pos in (0, 1):
for speed in (0, 1):
- res = dict((k, v) for k, v in agent.q_values.items() if k[1] == pos and k[2] == speed)
- print(f"pos:{pos}, speed:{speed}")
- acc = []
+ res = {k: v for k, v in agent.q_values.items() if k[1] == pos and k[2] == speed}
+ logger.info(f"pos:{pos}, speed:{speed}")
+ acc: list[np.floating] = []
for i in range(3):
col = [x[i] for x in res.values()]
acc.append(np.average(col))
- print(acc)
-
-
-if __name__ == "__main__":
- import os
- from pathlib import Path
-
- import pytest
-
- from crane_controller.crane_factory import build_crane # noqa: F401
-
- retcode = pytest.main(["-rP -s -v", "--show", "False", __file__])
- assert retcode == 0, f"Return code {retcode}"
- os.chdir(Path(__file__).parent.absolute() / "test_working_directory")
- # test_smoke(build_crane, show=True)
- # test_q_analyse(build_crane)
+ logger.info(f"averages: {acc}")
diff --git a/tests/test_working_directory/.gitignore b/tests/test_working_directory/.gitignore
new file mode 100644
index 0000000..1be5c5b
--- /dev/null
+++ b/tests/test_working_directory/.gitignore
@@ -0,0 +1 @@
+*test*.pdf
diff --git a/uv.lock b/uv.lock
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index 0000000..9e89acb
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