Johnny Fernandes
535 lines
20 KiB
Python
535 lines
20 KiB
Python
"""
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PPO training for the herding task with curriculum learning.
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Trains from scratch through a 1→max_sheep curriculum, evaluates after each
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stage, and auto-generates trajectory/timeseries plots plus a summary chart.
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Usage
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-----
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python train.py # defaults from config.json
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python train.py --config my_config.json --max-sheep 5
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python train.py --max-sheep 3 --steps-per-stage 1000000
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Outputs (in runs/<timestamp>/):
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config.json resolved config
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final_model.zip trained PPO model
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vecnorm.pkl VecNormalize statistics
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stage_results.json per-stage evaluation metrics
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success_rate.png summary bar chart
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eval/ trajectory & timeseries plots per sheep count
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"""
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import argparse
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import json
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import os
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import time
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from copy import deepcopy
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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import numpy as np
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from matplotlib.collections import LineCollection
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from stable_baselines3 import PPO
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from stable_baselines3.common.callbacks import BaseCallback
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from stable_baselines3.common.vec_env import (
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DummyVecEnv,
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SubprocVecEnv,
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VecNormalize,
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)
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from herding_env import HerdingEnv
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# ── Colours ──────────────────────────────────────────────────────────────────
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SHEEP_COLORS = [
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"#e41a1c", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00",
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"#a65628", "#f781bf", "#999999", "#66c2a5", "#fc8d62",
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]
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DOG_COLOR = "#4e342e"
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# ── Callbacks ────────────────────────────────────────────────────────────────
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class ProgressCallback(BaseCallback):
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"""One-line progress summary every `freq` env steps."""
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def __init__(self, stage_label: str, freq: int = 100_000):
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super().__init__()
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self.stage_label = stage_label
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self.freq = freq
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self._last = 0
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self._ep_returns = []
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self._ep_success = []
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self._total_eps = 0
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self._total_success = 0
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self._cur_ret = None
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def _on_step(self) -> bool:
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rewards = self.locals.get("rewards")
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dones = self.locals.get("dones")
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infos = self.locals.get("infos", [])
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if rewards is None or dones is None:
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return True
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if self._cur_ret is None or len(self._cur_ret) != len(rewards):
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self._cur_ret = np.zeros(len(rewards), dtype=np.float64)
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self._cur_ret += np.asarray(rewards, dtype=np.float64)
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for i, d in enumerate(dones):
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if not d:
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continue
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self._ep_returns.append(float(self._cur_ret[i]))
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info = infos[i] if i < len(infos) else {}
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success = int(info.get("n_penned", 0) == info.get("n_sheep", -1))
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self._ep_success.append(success)
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self._total_eps += 1
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self._total_success += success
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self._cur_ret[i] = 0.0
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if len(self._ep_returns) > 50:
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self._ep_returns.pop(0)
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self._ep_success.pop(0)
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if self.num_timesteps - self._last >= self.freq:
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self._last = self.num_timesteps
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n = len(self._ep_returns)
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mean_r = float(np.mean(self._ep_returns)) if n else float("nan")
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win_sr = float(np.mean(self._ep_success)) if n else float("nan")
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cum_sr = (self._total_success / self._total_eps
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if self._total_eps else float("nan"))
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print(f" ... [{self.stage_label} | "
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f"{self.num_timesteps:>7,} steps | "
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f"ret(last {n})={mean_r:+.2f} "
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f"win_sr={win_sr*100:.0f}% cum_sr={cum_sr*100:.0f}%]",
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flush=True)
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return True
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# ── Environment factory ──────────────────────────────────────────────────────
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def make_env(n_sheep, seed, max_steps, reward_cfg=None):
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def _init():
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env = HerdingEnv(n_sheep=n_sheep, max_steps=max_steps,
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reward_cfg=reward_cfg)
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env.reset(seed=seed)
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return env
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return _init
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# ── Failure-mode classification ──────────────────────────────────────────────
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COMPACT_RADIUS = 5.0
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def _classify(ep_radii, ep_com_dists, n_penned, n_sheep):
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if n_penned == n_sheep:
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return "SUCCESS"
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if min(ep_radii) > COMPACT_RADIUS:
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return "NEVER_COMPACT"
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first = next(i for i, r in enumerate(ep_radii) if r <= COMPACT_RADIUS)
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if min(ep_com_dists[first:]) > 3.0:
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return "COMPACT_CANT_DRIVE"
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if n_penned == 0:
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return "DROVE_NO_SHEEP"
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return f"PARTIAL_{n_penned}of{n_sheep}"
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# ── Evaluation ───────────────────────────────────────────────────────────────
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def evaluate(model, vn_template, n_sheep, n_episodes, max_steps,
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reward_cfg=None):
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"""Evaluate at a given sheep count; returns metrics dict."""
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raw = DummyVecEnv([make_env(n_sheep, 9999, max_steps, reward_cfg)])
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vn = VecNormalize(raw, norm_obs=True, norm_reward=False, training=False)
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vn.obs_rms = deepcopy(vn_template.obs_rms)
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vn.ret_rms = deepcopy(vn_template.ret_rms)
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successes = 0
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ep_lens = []
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min_pen_list = []
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action_mags = []
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failure_counts = {}
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rc_sums = {}
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rc_n = 0
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for _ in range(n_episodes):
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obs = vn.reset()
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done = False
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steps = 0
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min_pen = float("inf")
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mags = []
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ep_radii = []
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ep_com_dists = []
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while not done:
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action, _ = model.predict(obs, deterministic=True)
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obs, _, dones, infos = vn.step(action)
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done = dones[0]
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inner = vn.envs[0]
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com, radius, _ = inner._flock_stats()
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min_pen = min(min_pen, float(np.linalg.norm(com - inner.PEN_CENTER)))
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mags.append(float(np.linalg.norm(action[0])))
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ep_radii.append(radius)
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ep_com_dists.append(float(np.linalg.norm(com - inner.PEN_CENTER)))
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steps += 1
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rc = infos[0].get("rcomps")
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if rc:
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for k, v in rc.items():
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rc_sums[k] = rc_sums.get(k, 0.0) + v
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rc_n += 1
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n_penned = infos[0].get("n_penned", 0)
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success = n_penned == n_sheep
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successes += int(success)
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ep_lens.append(steps)
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min_pen_list.append(min_pen)
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action_mags.extend(mags)
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mode = _classify(ep_radii, ep_com_dists, n_penned, n_sheep)
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failure_counts[mode] = failure_counts.get(mode, 0) + 1
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vn.close()
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result = {
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"sr": successes / n_episodes,
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"mean_len": float(np.mean(ep_lens)),
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"mean_min_pen": float(np.mean(min_pen_list)),
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"mean_act": float(np.mean(action_mags)) if action_mags else 0.0,
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"failure_modes": failure_counts,
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}
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if rc_n > 0:
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result["reward_per_step"] = {k: v / rc_n for k, v in rc_sums.items()}
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return result
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# ── Visualization helpers ────────────────────────────────────────────────────
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def _draw_field(ax):
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ax.set_xlim(-16, 16)
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ax.set_ylim(-16, 16)
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ax.set_aspect("equal")
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ax.set_facecolor("#dcedc8")
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ax.add_patch(mpatches.Rectangle((-15, -15), 30, 30,
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fill=False, edgecolor="#795548", lw=2))
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ax.add_patch(mpatches.Rectangle((10, -15), 3, 7,
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facecolor="#ffe082", edgecolor="#795548", lw=2))
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ax.text(11.5, -11.5, "pen", ha="center", va="center",
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fontsize=8, color="#795548")
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def _faded_path(ax, xs, ys, color, lw=1.5, label=None):
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n = len(xs)
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if n < 2:
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return
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points = np.array([xs, ys]).T.reshape(-1, 1, 2)
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segs = np.concatenate([points[:-1], points[1:]], axis=1)
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alphas = np.linspace(0.15, 1.0, len(segs))
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colors = [(*matplotlib.colors.to_rgb(color), a) for a in alphas]
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ax.add_collection(LineCollection(segs, colors=colors, linewidth=lw))
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if label:
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ax.plot([], [], color=color, lw=lw, label=label)
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def run_and_record(model, vn_template, n_sheep, max_steps,
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reward_cfg=None, seed=42):
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"""Run one deterministic episode and return full history."""
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raw = DummyVecEnv([make_env(n_sheep, seed, max_steps, reward_cfg)])
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vn = VecNormalize(raw, norm_obs=True, norm_reward=False, training=False)
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vn.obs_rms = deepcopy(vn_template.obs_rms)
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vn.ret_rms = deepcopy(vn_template.ret_rms)
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obs = vn.reset()
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inner = vn.envs[0]
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done = False
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dog_xs, dog_ys = [], []
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sheep_xs = [[] for _ in range(n_sheep)]
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sheep_ys = [[] for _ in range(n_sheep)]
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radii = []
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pen_dists = [[] for _ in range(n_sheep)]
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action_mags = []
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rewards = []
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penned_at = [None] * n_sheep
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step = 0
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while not done:
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action, _ = model.predict(obs, deterministic=True)
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obs, reward, dones, infos = vn.step(action)
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done = dones[0]
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step += 1
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dog_xs.append(float(inner.dog_pos[0]))
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dog_ys.append(float(inner.dog_pos[1]))
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com, radius, _ = inner._flock_stats()
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radii.append(radius)
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rewards.append(float(reward[0]))
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action_mags.append(float(np.linalg.norm(action[0])))
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for i in range(n_sheep):
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sheep_xs[i].append(float(inner.sheep_pos[i][0]))
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sheep_ys[i].append(float(inner.sheep_pos[i][1]))
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pen_dists[i].append(
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float(np.linalg.norm(inner.sheep_pos[i] - inner.PEN_CENTER)))
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if inner.penned[i] and penned_at[i] is None:
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penned_at[i] = step
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n_penned = infos[0].get("n_penned", 0)
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vn.close()
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return dict(
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dog_xs=dog_xs, dog_ys=dog_ys,
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sheep_xs=sheep_xs, sheep_ys=sheep_ys,
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radii=radii, pen_dists=pen_dists,
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action_mags=action_mags, rewards=rewards,
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penned_at=penned_at,
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n_penned=n_penned, n_sheep=n_sheep,
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success=n_penned == n_sheep, steps=step,
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)
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def plot_trajectory(hist, out_path):
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fig, ax = plt.subplots(figsize=(7, 7))
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_draw_field(ax)
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for i in range(hist["n_sheep"]):
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c = SHEEP_COLORS[i % len(SHEEP_COLORS)]
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xs, ys = hist["sheep_xs"][i], hist["sheep_ys"][i]
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_faded_path(ax, xs, ys, c, lw=1.2, label=f"sheep {i+1}")
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ax.plot(xs[0], ys[0], "o", color=c, ms=7, zorder=4)
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end = hist["penned_at"][i] if hist["penned_at"][i] is not None else -1
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ax.plot(xs[end], ys[end], "*", color=c, ms=11, zorder=5)
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_faded_path(ax, hist["dog_xs"], hist["dog_ys"], DOG_COLOR, lw=2.0,
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label="dog")
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ax.plot(hist["dog_xs"][0], hist["dog_ys"][0], "s", color=DOG_COLOR,
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ms=10, zorder=5)
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ax.plot(hist["dog_xs"][-1], hist["dog_ys"][-1], "D", color=DOG_COLOR,
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ms=10, zorder=5)
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result = ("SUCCESS" if hist["success"]
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else f"FAIL ({hist['n_penned']}/{hist['n_sheep']})")
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ax.set_title(f"n={hist['n_sheep']} {result} {hist['steps']} steps",
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fontsize=12)
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ax.legend(loc="upper left", fontsize=8)
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plt.tight_layout()
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fig.savefig(out_path, dpi=120)
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plt.close(fig)
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def plot_timeseries(hist, out_path):
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t = np.arange(hist["steps"])
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fig, axes = plt.subplots(4, 1, figsize=(12, 10), sharex=True)
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axes[0].plot(t, hist["radii"], color="steelblue")
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axes[0].axhline(5.0, color="orange", ls="--", lw=1, label="compact (5m)")
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axes[0].set_ylabel("flock radius (m)")
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axes[0].legend(fontsize=8)
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axes[0].set_title("Flock radius")
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for i in range(hist["n_sheep"]):
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c = SHEEP_COLORS[i % len(SHEEP_COLORS)]
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axes[1].plot(t, hist["pen_dists"][i], color=c, lw=1,
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label=f"sheep {i+1}")
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if hist["penned_at"][i] is not None:
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axes[1].axvline(hist["penned_at"][i], color=c, ls=":", lw=1)
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axes[1].set_ylabel("dist to pen (m)")
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axes[1].legend(fontsize=7, ncol=min(hist["n_sheep"], 5))
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axes[1].set_title("Per-sheep distance to pen")
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axes[2].plot(t, hist["action_mags"], color="tomato", lw=1)
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axes[2].axhline(1.0, color="gray", ls="--", lw=1, label="max")
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axes[2].set_ylabel("action ||(vx,vy)||")
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axes[2].set_ylim(0, 1.5)
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axes[2].set_title("Dog action magnitude")
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axes[2].legend(fontsize=8)
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axes[3].plot(t, hist["rewards"], color="purple", lw=1, alpha=0.7)
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axes[3].axhline(0, color="black", lw=0.5)
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axes[3].set_ylabel("reward")
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axes[3].set_xlabel("step")
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axes[3].set_title("Reward per step")
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result = ("SUCCESS" if hist["success"]
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else f"FAIL ({hist['n_penned']}/{hist['n_sheep']})")
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fig.suptitle(f"n_sheep={hist['n_sheep']} {result} {hist['steps']} steps",
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fontsize=13)
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plt.tight_layout()
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fig.savefig(out_path, dpi=120)
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plt.close(fig)
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def plot_success_rate(stage_results, out_path):
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fig, ax = plt.subplots(figsize=(8, 4))
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ns = [r["n_sheep"] for r in stage_results]
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srs = [r["sr"] * 100 for r in stage_results]
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bars = ax.bar(ns, srs, color="steelblue", edgecolor="white")
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ax.set_xlabel("Sheep count")
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ax.set_ylabel("Success rate (%)")
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ax.set_ylim(0, 105)
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ax.axhline(90, color="orange", ls="--", lw=1, label="90% target")
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for bar, sr in zip(bars, srs):
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ax.text(bar.get_x() + bar.get_width() / 2,
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bar.get_height() + 1, f"{sr:.0f}%",
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ha="center", fontsize=9)
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ax.legend()
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ax.set_title("Evaluation success rate per sheep count")
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plt.tight_layout()
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fig.savefig(out_path, dpi=120)
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plt.close(fig)
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# ── CLI ──────────────────────────────────────────────────────────────────────
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DEFAULT_CONFIG = {
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"W_PER_SHEEP": 2.0,
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"W_ALIGN": 0.05,
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"W_PEN_BONUS": 10.0,
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"W_COMPLETE": 100.0,
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"W_STEP_COST": 0.02,
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"W_COMPACT": 0.0,
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"W_WALL_TOUCH": 0.04,
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"WALL_TOUCH_BUFFER": 0.3,
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"ALIGN_SHAPE": "standoff",
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"ALIGN_GATED": True,
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"ENTRY_AWARE": False,
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"ent_coef": 0.02,
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}
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def parse_args():
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p = argparse.ArgumentParser(
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description="PPO training for herding task with curriculum learning")
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p.add_argument("--config", type=str, default=None,
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help="JSON config file (reward weights + ent_coef)")
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p.add_argument("--max-sheep", type=int, default=10)
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p.add_argument("--steps-per-stage", type=int, default=1_500_000)
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p.add_argument("--n-envs", type=int, default=8)
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p.add_argument("--max-steps", type=int, default=2500)
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p.add_argument("--eval-episodes", type=int, default=30)
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p.add_argument("--run-dir", type=str, default=None)
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return p.parse_args()
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# ── Main ─────────────────────────────────────────────────────────────────────
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def main():
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args = parse_args()
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# Load config: --config overrides, else auto-load config.json if present
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cfg = dict(DEFAULT_CONFIG)
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config_path = args.config
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if config_path is None and os.path.exists("config.json"):
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config_path = "config.json"
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if config_path:
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with open(config_path) as f:
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cfg.update(json.load(f))
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print(f"Config loaded from {config_path}")
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rcfg = {k: v for k, v in cfg.items() if hasattr(HerdingEnv, k)}
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# Run directory
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run_dir = args.run_dir or os.path.join(
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"runs", time.strftime("%Y%m%d_%H%M%S"))
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eval_dir = os.path.join(run_dir, "eval")
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os.makedirs(eval_dir, exist_ok=True)
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with open(os.path.join(run_dir, "config.json"), "w") as f:
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json.dump(cfg, f, indent=2)
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print(f"Config: {cfg}")
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print(f"Run dir: {run_dir}")
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print(f"Curriculum: 1 → {args.max_sheep} sheep, "
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f"{args.steps_per_stage:,} steps/stage\n")
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|
|
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# Training envs
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train_env = SubprocVecEnv([
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make_env(1, seed=i, max_steps=args.max_steps, reward_cfg=rcfg)
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for i in range(args.n_envs)
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|
])
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vn = VecNormalize(train_env, norm_obs=True, norm_reward=True,
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clip_obs=10.0)
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|
|
|
# Model
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model = PPO(
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"MlpPolicy", vn,
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learning_rate=3e-4, n_steps=2048, batch_size=256, n_epochs=10,
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|
gamma=0.995, gae_lambda=0.95, clip_range=0.2,
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|
ent_coef=cfg.get("ent_coef", 0.02), vf_coef=0.5, max_grad_norm=0.5,
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policy_kwargs=dict(net_arch=[256, 256]),
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|
verbose=0,
|
|
)
|
|
|
|
# Curriculum training
|
|
stage_results = []
|
|
t0 = time.time()
|
|
|
|
try:
|
|
for n in range(1, args.max_sheep + 1):
|
|
if n > 1:
|
|
vn.env_method("set_n_sheep", n)
|
|
|
|
print(f"\n[Stage n_sheep={n}] training {args.steps_per_stage:,} steps")
|
|
model.learn(
|
|
total_timesteps=args.steps_per_stage,
|
|
reset_num_timesteps=(n == 1),
|
|
callback=ProgressCallback(f"{n} sheep", freq=100_000),
|
|
)
|
|
|
|
# Evaluate
|
|
print(f"[Stage n_sheep={n}] evaluating {args.eval_episodes} eps")
|
|
r = evaluate(model, vn, n, args.eval_episodes, args.max_steps, rcfg)
|
|
print(f"[Stage n_sheep={n}] sr={r['sr']*100:.0f}% "
|
|
f"mean_len={r['mean_len']:.0f} "
|
|
f"mean_min_pen={r['mean_min_pen']:.1f}m "
|
|
f"mean_act={r['mean_act']:.2f}")
|
|
|
|
# Failure-mode breakdown
|
|
if r["failure_modes"]:
|
|
modes = " ".join(
|
|
f"{k}={v}" for k, v in sorted(
|
|
r["failure_modes"].items(), key=lambda x: -x[1]))
|
|
print(f" failure modes: {modes}")
|
|
|
|
# Reward breakdown
|
|
if "reward_per_step" in r:
|
|
rps = r["reward_per_step"]
|
|
print(f" reward/step: " + " ".join(
|
|
f"{k}={v:+.4f}" for k, v in rps.items()))
|
|
|
|
# Episode visualization
|
|
hist = run_and_record(model, vn, n, args.max_steps, rcfg,
|
|
seed=1000 + n)
|
|
tag = "success" if hist["success"] else "fail"
|
|
plot_trajectory(
|
|
hist,
|
|
os.path.join(eval_dir, f"traj_{n}s_{tag}.png"))
|
|
plot_timeseries(
|
|
hist,
|
|
os.path.join(eval_dir, f"ts_{n}s_{tag}.png"))
|
|
|
|
r["n_sheep"] = n
|
|
stage_results.append(r)
|
|
|
|
# Save artefacts
|
|
model.save(os.path.join(run_dir, "final_model"))
|
|
vn.save(os.path.join(run_dir, "vecnorm.pkl"))
|
|
with open(os.path.join(run_dir, "stage_results.json"), "w") as f:
|
|
json.dump(stage_results, f, indent=2)
|
|
|
|
finally:
|
|
try:
|
|
vn.close()
|
|
except Exception:
|
|
pass
|
|
|
|
# Summary
|
|
elapsed = (time.time() - t0) / 60
|
|
print("\n" + "=" * 70)
|
|
print(" TRAINING SUMMARY")
|
|
print("=" * 70)
|
|
for r in stage_results:
|
|
print(f" n_sheep={r['n_sheep']} sr={r['sr']*100:>3.0f}% "
|
|
f"len={r['mean_len']:>5.0f} min_pen={r['mean_min_pen']:>5.1f}m "
|
|
f"act={r['mean_act']:.2f}")
|
|
print(f"\n Total time: {elapsed:.1f} min")
|
|
print(f" Artefacts: {run_dir}/")
|
|
|
|
plot_success_rate(stage_results, os.path.join(run_dir, "success_rate.png"))
|
|
print(f" Plots: {run_dir}/success_rate.png, {eval_dir}/")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|