Add deterministic multi-worker evaluation and seeded failure replay - #533
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…on sdc crash in sdc_only mode)
…c log, freeze on episode end
…interactive replay
…ng and rendering processes
…aining evaluation logic
- Simplified file handling in `make_client` function in `render.h`. - Removed deprecated `compare_episode_metrics.py` script. - Added `benchmark_comparison.py` to aggregate benchmark results. - Created `run_all_eval.sh` for streamlined evaluation of experiments. - Introduced `run_all_latest_eval.py` to execute evaluations on the latest models. - Implemented `run_failure_scenarios.py` to rerun failed scenarios based on metrics. - Updated `launch_nightly_best.sh` script comments for clarity. - Modified `nightly_report.py` to reflect new evaluation metrics. - Enhanced unit tests in `test_drive_config.py` to validate configuration files. - Added new test in `test_eval_zlib.py` to ensure agent capacity constraints are enforced.
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…, womd_single...) + eval during training should be a train arg in yaml not an eval, also modified
…re training defaults that were modified by the commit
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Adds catalog-driven, multi-worker benchmark evaluation with deterministic per-scenario seeds
and reproducible map selection.
Captures evaluation metrics and compact replay data, enabling failure scenarios to be
replayed and rendered interactively.
Here are a fews commands you can try out:
To Eval and then render failures :
load_model_path=experiments/mimolette/models/model_puffer_drive_003815.pt
eval.datasets=carla_fast
eval.render_failures=true
eval.render_obs=false
To eval and render all scenarios
load_model_path=experiments/mimolette/models/model_puffer_drive_003815.pt
eval.datasets=carla_fast \
eval.render_scenarios=true
eval.render_obs=false