mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 08:33:36 +00:00
271 lines
9.3 KiB
Python
271 lines
9.3 KiB
Python
import gymnasium as gym
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from gymnasium import spaces
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import numpy as np
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from .engine import Limbo, MarketEngine, PricingEngine
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from .lib.render import DashboardRenderer
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from .lib.coi import (
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compute_coi_leakage,
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compute_erosion_metrics,
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compute_agent_probability,
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)
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from .lib.behavior import get_transition_models, trajectory_to_events
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from .lib.wrappers import EconomicMetricsWrapper
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class PHANTOM(gym.Env):
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"""Gymnasium wrapper for Limbo pricing-market simulation implementing thesis COI framework
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reward = R(p,d) - λ·COI_leak(p,τ') per thesis Section on DR-RL
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COI_leak uses behavioral divergence to estimate agent probability f(τ')
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"""
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metadata = {"render_modes": ["human", "ansi"]}
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def __init__(
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self,
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n_products: int = 10,
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alpha: float = 0.3,
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N: int = 100,
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human_params: tuple = (50.0, 10.0),
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agent_params: tuple = (45.0, 15.0),
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noise_std: float = 1.0,
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price_bounds: tuple = (10.0, 150.0),
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lambda_coi: float = 0.1,
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coi_window: int = 10,
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render_mode: str = None,
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):
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super().__init__()
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self.n_products = n_products
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self.price_bounds = price_bounds
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self.lambda_coi = lambda_coi
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self.coi_window = coi_window
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self.render_mode = render_mode
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self.alpha = alpha
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self.N = N
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self.human_params = human_params
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self.agent_params = agent_params
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self.market = MarketEngine(
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alpha=alpha,
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N=N,
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human_params=human_params,
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agent_params=agent_params,
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noise_std=noise_std,
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)
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self._platform_stub = PricingEngine()
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self._limbo = Limbo(self._platform_stub, self.market)
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self.action_space = spaces.Box(
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low=price_bounds[0],
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high=price_bounds[1],
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shape=(n_products,),
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dtype=np.float32,
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)
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self.observation_space = spaces.Dict(
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{
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"demand": spaces.Box(
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low=0.0, high=100.0, shape=(n_products,), dtype=np.float32
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),
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"prices": spaces.Box(
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low=price_bounds[0],
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high=price_bounds[1],
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shape=(n_products,),
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dtype=np.float32,
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),
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}
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)
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self._prices = None
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self._demand = None
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self._step_count = 0
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self._demand_history = []
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self._price_history = []
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self._revenue_history = []
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self._renderer = None
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self._initial_episode_prices = None
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self._trajectories = [] # session trajectories for agent prob calculation
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# load behavioral models for agent probability estimation
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try:
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self._human_trans, self._agent_trans = get_transition_models()
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except Exception:
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# fallback if behavioral data unavailable
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self._human_trans, self._agent_trans = None, None
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def _get_obs(self) -> dict:
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demand_arr = np.array(
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[self._demand.get(i, 0.0) for i in range(self.n_products)], dtype=np.float32
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)
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return {"demand": demand_arr, "prices": self._prices.astype(np.float32)}
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def _compute_agent_prob(self) -> float:
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"""estimate agent probability from accumulated trajectories using KL divergence"""
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if (
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not self._trajectories
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or self._human_trans is None
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or self._agent_trans is None
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):
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return self.alpha # fallback to contamination level
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# aggregate all trajectories from this episode
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all_events = []
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for traj in self._trajectories:
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all_events.extend(trajectory_to_events(traj))
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if len(all_events) < 2:
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return self.alpha
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return compute_agent_probability(
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all_events, self._human_trans, self._agent_trans
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)
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def _compute_reward(self, prices: np.ndarray, demand: dict) -> float:
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revenue = np.sum(
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prices * np.array([demand.get(i, 0.0) for i in range(self.n_products)])
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)
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# compute agent probability from behavioral trajectories
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agent_prob = self._compute_agent_prob()
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# COI leakage: minimal implementation per thesis
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coi_leakage = compute_coi_leakage(prices, agent_prob)
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coi_penalty = self.lambda_coi * coi_leakage
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return float(revenue - coi_penalty)
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def _record_history(self):
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demand_arr = np.array(
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[self._demand.get(i, 0.0) for i in range(self.n_products)]
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)
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self._demand_history.append(demand_arr)
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self._price_history.append(self._prices.copy())
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self._revenue_history.append(np.sum(self._prices * demand_arr))
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def reset(self, seed=None, options=None):
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super().reset(seed=seed)
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self._prices = np.random.uniform(*self.price_bounds, size=self.n_products)
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self._initial_episode_prices = self._prices.copy()
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self._demand = self.market.act(self._prices)
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self._step_count = 0
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self._demand_history, self._price_history, self._revenue_history = [], [], []
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self._trajectories = []
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self._record_history()
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return self._get_obs(), {}
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def step(self, action: np.ndarray):
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self._prices = np.clip(action, *self.price_bounds)
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self._demand = self.market.act(self._prices)
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self._step_count += 1
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self._record_history()
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# capture trajectories generated by market for agent prob estimation
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if hasattr(self.market, "last_trajectories"):
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self._trajectories.extend(self.market.last_trajectories)
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agent_prob = self._compute_agent_prob()
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coi_leakage = compute_coi_leakage(self._prices, agent_prob)
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reward = self._compute_reward(self._prices, self._demand)
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terminated = self._step_count >= 100
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# legacy erosion metrics for comparison
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erosion = compute_erosion_metrics(
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self._price_history,
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self._demand_history,
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self._initial_episode_prices,
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self._prices,
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self.price_bounds,
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self.alpha,
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self.coi_window,
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)
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info = {
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"step": self._step_count,
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"agent_prob": agent_prob,
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"coi_leakage": coi_leakage,
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"coi_penalty": self.lambda_coi * coi_leakage,
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"erosion_metrics": erosion,
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"raw_revenue": np.sum(
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self._prices
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* np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])
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),
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}
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return self._get_obs(), reward, terminated, False, info
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def _compute_elasticity(self) -> np.ndarray:
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"""point elasticity: e = (dQ/dP) * (P/Q) via finite differences, clipped to [-5, 5]"""
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if len(self._price_history) < 2:
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return np.zeros(self.n_products)
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p, q = np.array(self._price_history), np.array(self._demand_history)
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dp, dq = np.diff(p, axis=0), np.diff(q, axis=0)
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valid = np.abs(dp) > 0.5
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with np.errstate(divide="ignore", invalid="ignore"):
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elasticity = np.where(
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valid, (dq / dp) * (p[:-1] / np.maximum(q[:-1], 1.0)), 0.0
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)
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elasticity = np.nan_to_num(np.clip(elasticity, -5.0, 5.0), nan=0.0)
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return (
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np.mean(elasticity, axis=0)
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if len(elasticity) > 0
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else np.zeros(self.n_products)
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)
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def render(self):
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if self.render_mode == "human":
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if self._renderer is None:
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self._renderer = DashboardRenderer()
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self._renderer.render(self)
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elif self.render_mode == "ansi":
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return (
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f"step={self._step_count}, prices={self._prices}, demand={self._demand}"
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)
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return None
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def close(self):
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if self._renderer:
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self._renderer.close()
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self._renderer = None
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if __name__ == "__main__":
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import wandb
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from .lib import MetricsCallback
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class RandomPolicy:
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"""Minimal SB3-compatible random policy for baseline testing."""
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def __init__(self, env):
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self.env = env
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self.num_timesteps = 0
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def learn(self, total_timesteps, callback=None):
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callback.model = self
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callback.num_timesteps = 0
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callback.locals = {}
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callback.on_training_start({}, {})
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obs, _ = self.env.reset()
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for step in range(total_timesteps):
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action = self.env.action_space.sample()
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obs, reward, term, trunc, info = self.env.step(action)
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self.num_timesteps = step + 1
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callback.num_timesteps = self.num_timesteps
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callback.locals = {"infos": [info]}
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callback.on_step()
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if term or trunc:
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callback.on_rollout_end()
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obs, _ = self.env.reset()
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return self
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def predict(self, obs, **kwargs):
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return self.env.action_space.sample(), None
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wandb.init(project="phantom-pricing", config={"policy": "random", "alpha": 0.3})
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env = EconomicMetricsWrapper(PHANTOM(n_products=15, alpha=0.3, render_mode=None))
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model = RandomPolicy(env)
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model.learn(total_timesteps=1000, callback=MetricsCallback())
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print(f"Episode revenue: {env.episode_revenue:.1f}")
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wandb.finish()
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env.close()
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