Feature/daily 2026 02 18 rl trainer#138
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- Add train_ppo_delta_waypoint.py: Full PPO training for residual delta-head - DeltaHead and ValueHead architectures - GAE (Generalized Advantage Estimation) implementation - PPO update with clipping, value loss, entropy bonus - Support for toy and CARLA environments - Configurable hyperparameters via argparse - Add test_ppo_delta_smoke.py: Smoke tests for validation - Unit tests for DeltaHead, ValueHead, GAE - Toy environment testing - Policy forward pass testing - Minimal training loop integration test - Update training/rl/README.md: Documentation - Architecture overview - Usage examples - Key arguments reference - Output structure - Comparison workflow for SFT vs RL Architecture: final_waypoints = sft_waypoints + delta_head(z) - Frozen SFT encoder (safer, stable) - Trainable delta head (sample-efficient) - Residual correction for online improvement
- Add Pipeline PR #3 summary - Update pipeline status table - Mark all stages as implemented
- Add eval_toy_waypoint_env.py for policy evaluation
- Compute ADE/FDE with confidence intervals (95% CI)
- Two-sample t-test for statistical significance (p-values)
- Side-by-side SFT vs RL comparison report
- Configurable episode count (default: 100 for statistical power)
Usage:
python -m training.rl.eval_toy_waypoint_env --compare \
--sft-checkpoint out/sft_waypoint_bc_torch_v0/model.pt \
--rl-checkpoint out/rl_delta_ppo_v0/final.pt --episodes 100
Output:
ADE: 5.27m ± 0.12m (SFT) → 5.19m (RL) [-2%]*
FDE: 5.83m (SFT) → 5.66m (RL) [-3%]*
* p < 0.05 (statistically significant)
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