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fix: coi better defined and aligned and sac improved

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Daniel Rosel
2026-01-25 10:36:37 +01:00
parent 1224841a82
commit 98a9a3738c
3 changed files with 5 additions and 5 deletions
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5
lab/case/thesis/simplified.py
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@@ -14,7 +14,7 @@ from dataclasses import dataclass, field
from typing import Dict, List, Tuple from typing import Dict, List, Tuple
import numpy as np import numpy as np
from .coi import COIWindow, compute_coi_window, coi_erosion from .coi import COIWindow, compute_coi_window
from .separability import TRANS_H, TRANS_A, kl_div, build_kernel, compute_divergence, estimate_alpha from .separability import TRANS_H, TRANS_A, kl_div, build_kernel, compute_divergence, estimate_alpha
ACTION_WEIGHTS = {"add_to_cart": 0.8, "checkout": 0.9, "purchase": 1.0, "view": 0.15, "detail": 0.25, "hover": 0.3, "start": 0.05, "end": 0.0} ACTION_WEIGHTS = {"add_to_cart": 0.8, "checkout": 0.9, "purchase": 1.0, "view": 0.15, "detail": 0.25, "hover": 0.3, "start": 0.05, "end": 0.0}
@@ -209,7 +209,8 @@ if __name__ == "__main__":
print(f'sessions: {len(sessions)}, agents: {sum(1 for s in sessions if s.actor=="A")}') print(f'sessions: {len(sessions)}, agents: {sum(1 for s in sessions if s.actor=="A")}')
for n in [1, 5, 10, 50, 100]: for n in [1, 5, 10, 50, 100]:
print(f'N={n:3d} agents -> COI erosion: {coi_erosion(n, price_std=5.0):.3f}') # theoretical: erosion = 1 - 2/(N+1) for uniform order statistic
print(f'N={n:3d} agents -> COI erosion: {1.0 - 2.0/(n+1):.3f}')
events = [Event('view', 0, 20.0, 0.1), Event('detail', 0, 20.0, 0.5), Event('cart', 0, 20.0, 1.0), Event('purchase', 0, 20.0, 2.0)] events = [Event('view', 0, 20.0, 0.1), Event('detail', 0, 20.0, 0.5), Event('cart', 0, 20.0, 1.0), Event('purchase', 0, 20.0, 2.0)]
print(f'human-like session alpha_hat: {estimate_alpha(Session(sid="test", events=events, actor="H")):.3f}') print(f'human-like session alpha_hat: {estimate_alpha(Session(sid="test", events=events, actor="H")):.3f}')

2
lab/case/thesis/simplified_env.py
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@@ -157,7 +157,7 @@ class PricingEnv(gym.Env if HAS_GYM else object):
"n_purchases": int(np.sum(purchases)), "n_purchases": int(np.sum(purchases)),
"avg_margin": float(np.mean((prices - self._sys.costs) / self._sys.costs)), "avg_margin": float(np.mean((prices - self._sys.costs) / self._sys.costs)),
"n_sessions": len(demand), "n_agents": n_agents, "price_std": float(np.std(prices)), "n_sessions": len(demand), "n_agents": n_agents, "price_std": float(np.std(prices)),
"coi_erosion": coi_erosion(max(1, n_agents), float(np.std(prices))), "coi_erosion": coi_erosion(coi.policy, coi.agent),
"coi_policy": float(coi.policy), "coi_agent": float(coi.agent), "coi_policy": float(coi.policy), "coi_agent": float(coi.agent),
"coi_leakage": float(coi.leak), "coi_survival": float(coi.survival_ratio), "coi_leakage": float(coi.leak), "coi_survival": float(coi.survival_ratio),
"cumulative_reward": sum(self._episode_rewards), "step": self._t, "cumulative_reward": sum(self._episode_rewards), "step": self._t,

3
lab/case/thesis/train.py
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@@ -28,7 +28,6 @@ except ImportError:
HAS_TB = False HAS_TB = False
from .simplified_env import PricingEnv, EnvConfig, make_env, adaptive_policy, fixed_price_policy, random_policy from .simplified_env import PricingEnv, EnvConfig, make_env, adaptive_policy, fixed_price_policy, random_policy
from .coi import coi_erosion
@dataclass @dataclass
@@ -214,7 +213,7 @@ def train(cfg: ExperimentConfig) -> Dict[str, Any]:
common = dict(verbose=1, seed=cfg.seed, tensorboard_log=str(log_path), device="auto") common = dict(verbose=1, seed=cfg.seed, tensorboard_log=str(log_path), device="auto")
model = { 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), "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=3e-4, buffer_size=100_000, batch_size=256, tau=0.005, gamma=0.99, **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), "a2c": lambda: A2C("MlpPolicy", train_env, learning_rate=7e-4, n_steps=5, gamma=0.99, **common),
}[cfg.algo.lower()]() }[cfg.algo.lower()]()