LSTM (RecurrentPPO) experiment + recurrent policy support
Adds RecurrentPPO-based training as an alternative to MLP+frame-stack. The LSTM gives the policy unbounded temporal memory, addressing the partial-obs failure mode of the 140° Webots LiDAR (tracker briefly empties when the dog turns; sporadic phantom tracks confuse decisions). * training/rl/train_lstm.py: from-scratch RecurrentPPO trainer (no BC init, no KL term since there's no reference). Uses HERDING_WEBOTS preset so the obs distribution matches deployment. * training/eval.py: auto-detects RecurrentPPO zips, maintains LSTM hidden state across steps, resets between episodes. * controllers/shepherd_dog/policy_loader.py: PolicyHandle supports recurrent policies — state managed inside, reset_recurrent() exposed. Result on diff/field after 3M steps: - Gym (default 360°): 69% avg success across n=1..10 - Gym (HERDING_WEBOTS preset, training env): 2% — penning 3-4/5 but rarely all 5 - Webots LiDAR 140°: 0/5 (same wall as DAgger and v1 policies) Conclusion: architectural changes (LSTM vs MLP) don't close the perception sim-to-real gap. The gym LiDAR sim doesn't faithfully reproduce Webots phantom-track distribution; any policy trained on the gym proxy fails to handle real Webots phantoms regardless of architecture. Closing this gap requires either modeling Webots phantom patterns in the gym sim (multi-day work) or Webots-in-the-loop training (very slow). See memory/lstm_results.md for details. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Johnny Fernandes
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"""Recurrent-PPO (LSTM) policy trainer for the herding env.
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Motivation
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----------
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The MLP+frame-stack policy struggles with partial observability under
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the 140° Webots LiDAR: the tracker briefly empties when the dog turns,
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and sporadic FP tracks at static features confuse the policy. An LSTM
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gives the policy unbounded temporal memory so it can:
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* keep modelling sheep positions when the tracker briefly drops them,
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* distinguish persistent (real) tracks from intermittent (phantom) ones.
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This is the literature-correct fix for partial-observability + noisy
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perception. Trains from scratch (no BC init) using vanilla PPO without
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the KL-to-reference term (no reference exists when starting clean).
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Usage
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-----
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python -m training.rl.train_lstm \\
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--out training/runs/lstm_differential_field \\
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--drive-mode differential --world field \\
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--total-timesteps 3000000 \\
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--use-webots-preset --fp-rate 0.0 --action-smooth 0.55
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Frame stack is forced to 1 since the LSTM provides its own memory.
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"""
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from __future__ import annotations
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import argparse
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import os
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import time
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from pathlib import Path
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import numpy as np
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# Configure field geometry before other herding imports read it at module level.
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from herding.world.geometry import configure_from_args as _configure_from_args
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_configure_from_args()
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from sb3_contrib import RecurrentPPO
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from stable_baselines3.common.callbacks import EvalCallback
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from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
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from herding.world.geometry import MAX_SHEEP
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from training.herding_env import HerdingEnv
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def _make_env(rank: int, seed: int, drive_mode: str, difficulty: float,
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max_n_sheep: int, herding_cfg):
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def _init():
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env = HerdingEnv(
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max_n_sheep=max_n_sheep, difficulty=difficulty,
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seed=seed + rank, frame_stack=1, drive_mode=drive_mode,
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herding_cfg=herding_cfg,
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)
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return env
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return _init
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", required=True,
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help="Output directory for the LSTM policy.")
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parser.add_argument("--total-timesteps", type=int, default=3_000_000)
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parser.add_argument("--n-envs", type=int, default=8)
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parser.add_argument("--n-steps", type=int, default=256)
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parser.add_argument("--lstm-hidden", type=int, default=128)
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parser.add_argument("--lr", type=float, default=3e-4)
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--max-n-sheep", type=int, default=MAX_SHEEP)
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parser.add_argument("--difficulty", type=float, default=1.0)
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parser.add_argument("--drive-mode", default="differential",
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choices=["differential", "mecanum"])
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parser.add_argument("--world", default=None,
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choices=["field", "field_round"])
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parser.add_argument("--fp-rate", type=float, default=0.0)
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parser.add_argument("--action-smooth", type=float, default=0.55)
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parser.add_argument("--wheel-slip-std", type=float, default=0.05)
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parser.add_argument("--use-webots-preset", action="store_true",
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help="Train in the HERDING_WEBOTS env (140° FOV + tight tracker).")
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parser.add_argument("--device", default="cpu")
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args = parser.parse_args()
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from herding.config import HerdingConfig, HERDING_WEBOTS, DomainRandomConfig, RobotConfig
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if args.use_webots_preset:
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herding_cfg = HERDING_WEBOTS.replace(
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domain_random=DomainRandomConfig(
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fp_rate=args.fp_rate,
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wheel_slip_std=args.wheel_slip_std,
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),
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robot=RobotConfig(action_smooth=args.action_smooth),
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)
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print(f"[lstm] HERDING_WEBOTS preset + DR: fp_rate={args.fp_rate}")
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else:
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herding_cfg = None
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if args.fp_rate > 0.0 or args.action_smooth > 0.0 or args.wheel_slip_std > 0.0:
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herding_cfg = HerdingConfig(
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domain_random=DomainRandomConfig(
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fp_rate=args.fp_rate,
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wheel_slip_std=args.wheel_slip_std,
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),
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robot=RobotConfig(action_smooth=args.action_smooth),
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)
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env_fns = [_make_env(i, args.seed, args.drive_mode, args.difficulty,
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args.max_n_sheep, herding_cfg)
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for i in range(args.n_envs)]
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venv = SubprocVecEnv(env_fns) if args.n_envs > 1 else DummyVecEnv(env_fns)
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eval_venv = DummyVecEnv([_make_env(99, args.seed + 999, args.drive_mode,
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args.difficulty, args.max_n_sheep,
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herding_cfg)])
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out = Path(args.out)
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out.mkdir(parents=True, exist_ok=True)
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print(f"[lstm] drive_mode={args.drive_mode} world={os.environ.get('HERDING_WORLD', 'field')}")
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print(f"[lstm] total_timesteps={args.total_timesteps} n_envs={args.n_envs} "
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f"lr={args.lr} lstm_hidden={args.lstm_hidden}")
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model = RecurrentPPO(
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"MlpLstmPolicy", venv,
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learning_rate=args.lr,
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n_steps=args.n_steps,
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batch_size=args.n_steps, # full rollout = one batch (matches LSTM episode boundaries)
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n_epochs=4,
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gamma=0.99,
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gae_lambda=0.95,
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clip_range=0.2,
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ent_coef=0.0,
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max_grad_norm=0.5,
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policy_kwargs=dict(
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net_arch=dict(pi=[256, 256], vf=[256, 256]),
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lstm_hidden_size=args.lstm_hidden,
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n_lstm_layers=1,
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shared_lstm=False,
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enable_critic_lstm=True,
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),
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device=args.device,
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verbose=1,
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seed=args.seed,
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tensorboard_log=str(out / "tb"),
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)
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eval_cb = EvalCallback(
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eval_venv,
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best_model_save_path=str(out / "best"),
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log_path=str(out / "evals"),
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eval_freq=max(args.n_steps * args.n_envs, 20_000) // args.n_envs,
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n_eval_episodes=5,
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deterministic=True,
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render=False,
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)
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t0 = time.time()
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model.learn(total_timesteps=args.total_timesteps, callback=eval_cb,
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progress_bar=True)
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print(f"[lstm] training done in {time.time() - t0:.0f}s")
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# Save best (by eval) if it exists; otherwise save final.
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best = out / "best" / "best_model.zip"
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if best.exists():
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import shutil
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shutil.copy(best, out / "policy.zip")
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print(f"[lstm] best snapshot → {out / 'policy.zip'}")
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else:
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model.save(str(out / "policy.zip"))
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print(f"[lstm] no eval beat init; final snapshot → {out / 'policy.zip'}")
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model.save(str(out / "final.zip"))
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if __name__ == "__main__":
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main()
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