feat: consistent failure case

lab/case/thesis/train.py

@@ -2,11 +2,6 @@
 
 Trains pricing policies using stable-baselines3 with TensorBoard logging.
 Tracks COI erosion, alpha estimation error, and economic KPIs per thesis formulation.
-
-Usage:
-    python -m lab.case.thesis.train --algo ppo --alpha 0.3 --steps 100000
-    python -m lab.case.thesis.train --algo adaptive --sweep  # run alpha sweep
-    tensorboard --logdir lab/case/thesis/runs
 """
 from __future__ import annotations
 import argparse
@@ -41,9 +36,9 @@ class EpisodeMetrics:
     reward: float = 0.0
     revenue: float = 0.0
     profit: float = 0.0
-    coi_erosion: float = 0.0      # theorem 1: order statistic erosion
-    coi_leakage: float = 0.0      # per-step leakage penalty
-    alpha_error: float = 0.0      # |α - α̂|
+    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
@@ -213,6 +208,7 @@ def train(cfg: ExperimentConfig) -> Dict[str, Any]:
         if algo_cls is None:
             raise ValueError(f"unknown algo: {cfg.algo}")
         common = dict(verbose=1, seed=cfg.seed, tensorboard_log=str(log_path), device="auto")
+        # TODO: setup hyper parameter passing to train different variations (no free lunch)
         if cfg.algo.lower() == "ppo":
             model = PPO("MlpPolicy", train_env, learning_rate=3e-4, n_steps=2048,
                         batch_size=64, n_epochs=10, gamma=0.99, gae_lambda=0.95,