"""RL training for thesis pricing system with thesis-aligned metrics. Trains pricing policies using stable-baselines3 with TensorBoard logging. Tracks COI erosion, alpha estimation error, and economic KPIs per thesis formulation. """ from __future__ import annotations import argparse import json from concurrent.futures import ProcessPoolExecutor, as_completed from dataclasses import dataclass, asdict, field from pathlib import Path from typing import Dict, List, Callable, Any import numpy as np try: from stable_baselines3 import PPO, SAC, A2C from stable_baselines3.common.callbacks import BaseCallback, EvalCallback from stable_baselines3.common.vec_env import DummyVecEnv from stable_baselines3.common.monitor import Monitor HAS_SB3 = True except ImportError: HAS_SB3 = False try: from torch.utils.tensorboard import SummaryWriter HAS_TB = True except ImportError: HAS_TB = False from .simplified_env import PricingEnv, EnvConfig, make_env, adaptive_policy, fixed_price_policy, random_policy @dataclass class EpisodeMetrics: reward: float = 0.0 revenue: float = 0.0 profit: float = 0.0 coi_erosion: float = 0.0 coi_leakage: float = 0.0 alpha_error: float = 0.0 avg_margin: float = 0.0 n_agents: int = 0 steps: int = 0 def accumulate(self, info: Dict[str, Any]) -> None: self.steps += 1 self.reward += info.get('reward', 0) self.revenue += info.get('revenue', 0) self.profit += info.get('profit', 0) self.coi_erosion += info.get('coi_erosion', 0) self.coi_leakage += info.get('coi_leakage', 0) self.alpha_error += abs(info.get('alpha_true', 0) - info.get('alpha_est', 0)) self.avg_margin += info.get('avg_margin', 0) self.n_agents += info.get('n_agents', 0) def normalized(self) -> Dict[str, float]: s = max(self.steps, 1) return {k: getattr(self, k) / s for k in ['revenue', 'profit', 'coi_erosion', 'coi_leakage', 'alpha_error', 'avg_margin', 'n_agents']} @dataclass class ExperimentConfig: algo: str = "ppo" total_timesteps: int = 100_000 n_envs: int = 4 eval_freq: int = 5000 n_eval_episodes: int = 10 log_dir: str = "lab/case/thesis/runs" seed: int = 42 n_products: int = 10 max_steps: int = 200 alpha_true: float = 0.2 reward_mode: str = "robust" experiment_name: str | None = None def __post_init__(self): if self.experiment_name is None: self.experiment_name = f"{self.algo}_a{self.alpha_true:.2f}_{self.reward_mode}" class Policy: """Unified policy interface for baselines and trained models.""" def __init__(self, policy_fn: Callable[[np.ndarray, int], np.ndarray], name: str): self._fn, self.name = policy_fn, name def predict(self, obs: np.ndarray, deterministic: bool = True) -> tuple[np.ndarray, None]: return self._fn(obs, (len(obs) - 3) // 3), None @staticmethod def fixed(margin: float = 0.15) -> "Policy": return Policy(lambda obs, n: fixed_price_policy(np.ones(n), margin), f"fixed_{margin:.2f}") @staticmethod def adaptive(base_margin: float = 0.15) -> "Policy": return Policy(lambda obs, n: adaptive_policy(obs, n, base_margin), f"adaptive_{base_margin:.2f}") @staticmethod def random() -> "Policy": return Policy(lambda obs, n: random_policy(n), "random") @staticmethod def myopic(greed: float = 0.3) -> "Policy": def _fn(obs: np.ndarray, n: int) -> np.ndarray: demand_norm = obs[n:2*n] if len(obs) > 2*n else np.ones(n) * 0.5 return np.ones(n, dtype=np.float32) * np.clip(1.0 + greed * (1 + np.mean(demand_norm)), 0.5, 1.5) return Policy(_fn, f"myopic_{greed:.1f}") def log_metrics(writer: SummaryWriter | None, metrics: Dict[str, float], prefix: str, step: int) -> None: if writer is None: return for k, v in metrics.items(): writer.add_scalar(f'{prefix}/{k}', v, step) class MetricsCallback(BaseCallback): def __init__(self, writer: SummaryWriter | None, verbose: int = 0): super().__init__(verbose) self._writer = writer def _on_step(self) -> bool: if self._writer is None: return True for info in self.locals.get('infos', []): t = self.num_timesteps self._writer.add_scalar('economics/revenue', info.get('revenue', 0), t) self._writer.add_scalar('economics/profit', info.get('profit', 0), t) self._writer.add_scalar('economics/margin', info.get('avg_margin', 0), t) self._writer.add_scalar('coi/erosion', info.get('coi_erosion', 0), t) self._writer.add_scalar('coi/leakage', info.get('coi_leakage', 0), t) self._writer.add_scalar('alpha/estimation_error', abs(info.get('alpha_true', 0) - info.get('alpha_est', 0)), t) self._writer.add_scalar('agents/count', info.get('n_agents', 0), t) return True def make_vec_env(cfg: ExperimentConfig, n_envs: int = 1) -> DummyVecEnv: def _make(): return Monitor(make_env(EnvConfig(n_products=cfg.n_products, max_steps=cfg.max_steps, alpha_true=cfg.alpha_true, reward_mode=cfg.reward_mode, seed=cfg.seed))) return DummyVecEnv([_make for _ in range(n_envs)]) def run_episodes(policy: Policy | Any, env: PricingEnv, n_episodes: int) -> List[EpisodeMetrics]: """Run policy for n episodes and collect metrics.""" metrics = [] for _ in range(n_episodes): obs, _ = env.reset() ep, done = EpisodeMetrics(), False while not done: action, _ = policy.predict(obs, deterministic=True) obs, reward, term, trunc, info = env.step(action) done = term or trunc ep.accumulate(info) ep.reward += reward metrics.append(ep) return metrics def evaluate_policy(policy: Policy | Any, cfg: ExperimentConfig, n_episodes: int = 20) -> Dict[str, float]: env = make_env(EnvConfig(n_products=cfg.n_products, max_steps=cfg.max_steps, alpha_true=cfg.alpha_true, reward_mode=cfg.reward_mode, seed=cfg.seed + 999)) metrics = run_episodes(policy, env, n_episodes) return { 'reward_mean': np.mean([m.reward for m in metrics]), 'reward_std': np.std([m.reward for m in metrics]), **{f'{k}_mean': np.mean([m.normalized()[k] for m in metrics]) for k in ['revenue', 'profit', 'coi_erosion', 'coi_leakage', 'alpha_error', 'avg_margin']}, } def run_baseline(policy: Policy, vec_env: DummyVecEnv, total_steps: int, writer: SummaryWriter | None): obs, n_envs = vec_env.reset(), vec_env.num_envs ep_rewards = np.zeros(n_envs) for step in range(0, total_steps, n_envs): actions = np.array([policy.predict(obs[i])[0] for i in range(n_envs)]) obs, rewards, dones, infos = vec_env.step(actions) ep_rewards += rewards for i, info in enumerate(infos): if writer: writer.add_scalar('economics/revenue', info.get('revenue', 0), step) writer.add_scalar('economics/profit', info.get('profit', 0), step) writer.add_scalar('economics/margin', info.get('avg_margin', 0), step) writer.add_scalar('coi/erosion', info.get('coi_erosion', 0), step) writer.add_scalar('coi/leakage', info.get('coi_leakage', 0), step) writer.add_scalar('alpha/estimation_error', abs(info.get('alpha_true', 0) - info.get('alpha_est', 0)), step) writer.add_scalar('agents/count', info.get('n_agents', 0), step) if dones[i]: if writer: writer.add_scalar('rollout/ep_reward', ep_rewards[i], step) ep_rewards[i] = 0 def train(cfg: ExperimentConfig) -> Dict[str, Any]: is_baseline = cfg.algo.lower() in ["fixed", "adaptive", "random", "myopic"] if not HAS_SB3 and not is_baseline: raise ImportError("stable-baselines3 required: pip install stable-baselines3[extra]") log_path = Path(cfg.log_dir) / cfg.experiment_name log_path.mkdir(parents=True, exist_ok=True) with open(log_path / "config.json", "w") as f: json.dump(asdict(cfg), f, indent=2) writer = SummaryWriter(log_path) if HAS_TB else None train_env, eval_env = make_vec_env(cfg, cfg.n_envs), make_vec_env(cfg, 1) if is_baseline: policy = {"fixed": Policy.fixed, "adaptive": Policy.adaptive, "random": Policy.random, "myopic": Policy.myopic}[cfg.algo.lower()]() run_baseline(policy, train_env, cfg.total_timesteps, writer) final_metrics = evaluate_policy(policy, cfg) else: algo_cls = {"ppo": PPO, "sac": SAC, "a2c": A2C}[cfg.algo.lower()] common = dict(verbose=1, seed=cfg.seed, tensorboard_log=str(log_path), device="auto") model = { "ppo": lambda: PPO("MlpPolicy", train_env, learning_rate=3e-4, n_steps=2048, batch_size=64, n_epochs=10, gamma=0.99, gae_lambda=0.95, clip_range=0.2, ent_coef=0.01, **common), "sac": lambda: SAC("MlpPolicy", train_env, learning_rate=1e-4, buffer_size=50_000, batch_size=512, tau=0.02, gamma=0.99, learning_starts=1000, ent_coef="auto_0.1", train_freq=4, **common), "a2c": lambda: A2C("MlpPolicy", train_env, learning_rate=7e-4, n_steps=5, gamma=0.99, **common), }[cfg.algo.lower()]() cb = MetricsCallback(writer) eval_cb = EvalCallback(eval_env, best_model_save_path=str(log_path / "best"), log_path=str(log_path), eval_freq=cfg.eval_freq, n_eval_episodes=cfg.n_eval_episodes, deterministic=True) model.learn(cfg.total_timesteps, callback=[cb, eval_cb], progress_bar=True) model.save(log_path / "final_model") policy = model final_metrics = evaluate_policy(model, cfg) if writer: log_metrics(writer, final_metrics, 'final', cfg.total_timesteps) writer.close() train_env.close(); eval_env.close() with open(log_path / "results.json", "w") as f: json.dump(final_metrics, f, indent=2) return {"path": str(log_path), "metrics": final_metrics} def _train_alpha(args: tuple) -> tuple[str, Dict]: """Worker for parallel sweep - must be top-level for pickling.""" cfg_dict, alpha = args cfg_dict["alpha_true"] = alpha cfg_dict["experiment_name"] = f"{cfg_dict['algo']}_a{alpha:.2f}_{cfg_dict['reward_mode']}" sweep_cfg = ExperimentConfig(**cfg_dict) print(f"[alpha={alpha:.2f}] starting") metrics = train(sweep_cfg)["metrics"] print(f"[alpha={alpha:.2f}] done") return f"alpha_{alpha:.2f}", metrics def run_sweep(cfg: ExperimentConfig, alphas: List[float] | None = None, max_workers: int | None = None) -> Dict[str, Dict]: alphas = alphas or [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] cfg_dict = asdict(cfg) if max_workers == 1: # sequential fallback results = dict(_train_alpha((cfg_dict.copy(), a)) for a in alphas) else: with ProcessPoolExecutor(max_workers=max_workers) as pool: futures = {pool.submit(_train_alpha, (cfg_dict.copy(), a)): a for a in alphas} results = {} for fut in as_completed(futures): key, metrics = fut.result() results[key] = metrics summary_path = Path(cfg.log_dir) / f"sweep_{cfg.algo}_{cfg.reward_mode}.json" with open(summary_path, "w") as f: json.dump(results, f, indent=2) print(f"\nSweep results saved to {summary_path}") return results def _train_policy(args: tuple) -> tuple[str, Dict]: """Worker for parallel policy comparison.""" cfg_dict, algo = args cfg_dict["algo"] = algo cfg_dict["experiment_name"] = f"cmp_{algo}_a{cfg_dict['alpha_true']:.2f}" cmp_cfg = ExperimentConfig(**cfg_dict) print(f"[{algo}] starting") metrics = train(cmp_cfg)["metrics"] print(f"[{algo}] done") return algo, metrics def compare_policies(cfg: ExperimentConfig, policies: List[str] | None = None, max_workers: int | None = None) -> Dict[str, Dict]: policies = policies or ["fixed", "adaptive", "myopic", "random"] cfg_dict = asdict(cfg) if max_workers == 1: results = dict(_train_policy((cfg_dict.copy(), p)) for p in policies) else: with ProcessPoolExecutor(max_workers=max_workers) as pool: futures = {pool.submit(_train_policy, (cfg_dict.copy(), p)): p for p in policies} results = {} for fut in as_completed(futures): algo, metrics = fut.result() results[algo] = metrics cmp_path = Path(cfg.log_dir) / f"compare_a{cfg.alpha_true:.2f}.json" with open(cmp_path, "w") as f: json.dump(results, f, indent=2) print(f"\nComparison saved to {cmp_path}") for algo, m in results.items(): print(f" {algo:12s}: reward={m['reward_mean']:.2f} coi_erosion={m['coi_erosion_mean']:.4f} alpha_err={m['alpha_error_mean']:.4f}") return results def main(): parser = argparse.ArgumentParser(description="Train RL pricing policies") parser.add_argument("--algo", default="ppo", choices=["ppo", "sac", "a2c", "fixed", "adaptive", "random", "myopic"]) parser.add_argument("--steps", type=int, default=100_000) parser.add_argument("--alpha", type=float, default=0.2) parser.add_argument("--reward-mode", default="robust", choices=["revenue", "profit", "robust", "coi_aware"]) parser.add_argument("--n-products", type=int, default=10) parser.add_argument("--n-envs", type=int, default=4) parser.add_argument("--seed", type=int, default=42) parser.add_argument("--log-dir", default="lab/case/thesis/runs") parser.add_argument("--sweep", action="store_true", help="run contamination sweep") parser.add_argument("--compare", action="store_true", help="compare all baselines") parser.add_argument("--workers", type=int, default=None, help="max parallel workers for sweep (None=auto, 1=sequential)") args = parser.parse_args() cfg = ExperimentConfig(algo=args.algo, total_timesteps=args.steps, alpha_true=args.alpha, reward_mode=args.reward_mode, n_products=args.n_products, n_envs=args.n_envs, seed=args.seed, log_dir=args.log_dir) if args.sweep: run_sweep(cfg, max_workers=args.workers) elif args.compare: compare_policies(cfg, max_workers=args.workers) else: result = train(cfg) print(f"\nTraining complete: {result['path']}") print(f"Metrics: {json.dumps(result['metrics'], indent=2)}") if __name__ == "__main__": main()