mirror of
https://github.com/velocitatem/PHANTOM.git
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478 lines
19 KiB
Python
478 lines
19 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_uplift_coi,
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extract_purchases,
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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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from .jax.robust import select_adversarial_alpha_jax, _JAX_OK
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class _ActionPricingEngine(PricingEngine):
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def __init__(self, n_products: int, price_bounds: tuple):
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self._prices = np.full(n_products, price_bounds[0], dtype=float)
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def set_prices(self, prices: np.ndarray):
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self._prices = np.asarray(prices, dtype=float)
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def act(self, _):
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return self._prices
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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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robust inner step: min over alpha in Wasserstein interval around nominal alpha
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actions are discrete global price-scale moves
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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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robust_radius: float = 0.0,
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robust_points: int = 5,
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robust_rollouts: int = 1,
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info_value: float = 1.0,
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eta_ux: float = 0.5,
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reward_profit_weight: float = 1.0,
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action_levels: int = 9,
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action_scale_low: float = 0.9,
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action_scale_high: float = 1.1,
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max_steps: int = 100,
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margin_floor: float = 0.05,
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margin_floor_patience: int = 5,
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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.max_steps = max(1, int(max_steps))
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self.margin_floor = float(
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margin_floor
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) # terminate if avg margin stays below this for patience steps
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self.margin_floor_patience = max(1, int(margin_floor_patience))
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self.render_mode = render_mode
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self.alpha = float(alpha)
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self.nominal_alpha = float(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.robust_radius = max(0.0, float(robust_radius))
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self.robust_points = max(1, int(robust_points))
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self.robust_rollouts = max(1, int(robust_rollouts))
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self.info_value = float(info_value)
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self.eta_ux = float(eta_ux)
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self.reward_profit_weight = float(reward_profit_weight)
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self.action_levels = max(2, int(action_levels))
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self._action_scales = np.linspace(
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float(action_scale_low), float(action_scale_high), self.action_levels
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)
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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 = _ActionPricingEngine(n_products, price_bounds)
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self._limbo = Limbo(self._platform_stub, self.market)
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self._set_market_mix(self.nominal_alpha)
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self.action_space = spaces.Discrete(self.action_levels)
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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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"signals": spaces.Box(
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low=np.array([0.0, 0.0, 0.0, 0.0], dtype=np.float32),
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high=np.array([1.0, 1.0, 1.0, 1.0], dtype=np.float32),
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shape=(4,),
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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._global_step = 0 # monotonic; used as JAX RNG seed across resets
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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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self.baseline_prices = np.full(self.n_products, self.price_bounds[0])
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self.anchor_prices = np.full(
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self.n_products,
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float(np.clip(float(self.human_params[0]), *self.price_bounds)),
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)
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self.competitive_cap = float(
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min(self.price_bounds[1], float(np.mean(self.anchor_prices)) * 1.15)
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)
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self._low_margin_streak = 0 # consecutive steps below margin_floor
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self._last_agent_prob = float(self.alpha)
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self._last_alpha_adv = float(self.alpha)
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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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signals = np.array(
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[
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float(np.clip(self._last_agent_prob, 0.0, 1.0)),
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float(np.clip(self._last_alpha_adv, 0.0, 1.0)),
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float(np.clip(self.nominal_alpha, 0.0, 1.0)),
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float(np.clip(self.robust_radius, 0.0, 1.0)),
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],
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dtype=np.float32,
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)
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return {
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"demand": demand_arr,
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"prices": self._prices.astype(np.float32),
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"signals": signals,
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}
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def _set_market_mix(self, alpha: float):
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alpha = float(np.clip(alpha, 0.0, 1.0))
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n_agents = int(self.N * alpha)
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self.alpha = alpha
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self.market.alpha = alpha
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self.market.Nagents = n_agents
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self.market.Nhumans = self.N - n_agents
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def _decode_action(self, action) -> np.ndarray:
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prev = self._prices
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base = self.anchor_prices
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def _blend(target: np.ndarray) -> np.ndarray:
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if prev is None:
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lower = float(self.price_bounds[0])
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return np.clip(target, lower, self.competitive_cap)
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blended = 0.75 * np.asarray(prev, dtype=float) + 0.25 * target
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lower = float(self.price_bounds[0])
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return np.clip(blended, lower, self.competitive_cap)
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if np.isscalar(action):
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idx = int(np.clip(int(action), 0, self.action_levels - 1))
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target = base * self._action_scales[idx]
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return _blend(target)
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a = np.asarray(action)
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if a.size == 1:
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idx = int(np.clip(int(a.reshape(-1)[0]), 0, self.action_levels - 1))
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target = base * self._action_scales[idx]
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return _blend(target)
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lower = float(self.price_bounds[0])
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return np.clip(a.astype(float), lower, self.competitive_cap)
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def _compute_agent_prob(self, trajectories=None) -> float:
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trajectories = (
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self.market.last_trajectories if trajectories is None else trajectories
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)
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if not trajectories or self._human_trans is None or self._agent_trans is None:
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return float(self.market.alpha)
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probs = []
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for traj in trajectories:
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events = trajectory_to_events(traj)
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if len(events) < 2:
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continue
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probs.append(
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compute_agent_probability(events, self._human_trans, self._agent_trans)
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)
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return float(np.mean(probs)) if probs else float(self.market.alpha)
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def _compute_reward(
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self, prices: np.ndarray, demand: dict, agent_prob: float, trajectories: list
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) -> tuple[float, dict]:
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demand_arr = np.array(
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[demand.get(i, 0.0) for i in range(self.n_products)], dtype=float
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)
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revenue = float(np.dot(prices, demand_arr))
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floor_cost = float(np.dot(self.baseline_prices, demand_arr))
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profit = revenue - floor_cost
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purchases = extract_purchases(trajectories)
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coi_mix = compute_uplift_coi(prices, purchases, self.baseline_prices)
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coi_leakage = float(agent_prob * self.info_value)
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info_budget = max(floor_cost, 1.0)
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coi_penalty = self.lambda_coi * coi_leakage * info_budget
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if len(self._price_history) > 0:
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prev_prices = np.asarray(self._price_history[-1], dtype=float)
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rel_change = (prices - prev_prices) / np.maximum(prev_prices, 1.0)
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volatility = float(np.mean(np.abs(rel_change)))
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upward_volatility = float(np.mean(np.clip(rel_change, 0.0, None)))
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else:
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volatility = 0.0
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upward_volatility = 0.0
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ux_penalty = self.eta_ux * info_budget * (volatility + 0.5 * upward_volatility)
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competitive_anchor = float(np.mean(self.anchor_prices))
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price_ratio = prices / max(competitive_anchor, 1.0)
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supra_excess = np.clip(price_ratio - 1.15, 0.0, None)
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supra_penalty = 4.0 * info_budget * float(np.mean(np.square(supra_excess)))
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supra_share = float(np.mean(supra_excess > 0.0))
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reward_revenue = self.reward_profit_weight * profit
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reward = reward_revenue - coi_penalty - ux_penalty - supra_penalty
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return reward, {
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"revenue": revenue,
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"cost_floor": floor_cost,
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"profit": profit,
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"coi_mix": float(coi_mix),
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"coi_base": 0.0,
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"coi_leakage": coi_leakage,
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"coi_penalty": coi_penalty,
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"coi_info_budget": info_budget,
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"ux_penalty": ux_penalty,
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"volatility": volatility,
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"upward_volatility": upward_volatility,
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"supra_penalty": supra_penalty,
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"supra_share": supra_share,
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"competitive_anchor": competitive_anchor,
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"reward_revenue": reward_revenue,
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"reward_total": reward,
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}
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def _alpha_candidates(self) -> np.ndarray:
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if self.robust_radius <= 0.0 or self.robust_points == 1:
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return np.array([self.nominal_alpha], dtype=float)
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lo = max(0.0, self.nominal_alpha - self.robust_radius)
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hi = min(1.0, self.nominal_alpha + self.robust_radius)
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return np.linspace(lo, hi, self.robust_points)
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def _evaluate_candidate(self, alpha: float, prices: np.ndarray) -> float:
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self._set_market_mix(alpha)
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rewards = []
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for _ in range(self.robust_rollouts):
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demand = self.market.act(prices)
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trajectories = list(self.market.last_trajectories)
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agent_prob = self._compute_agent_prob(trajectories)
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reward, _ = self._compute_reward(prices, demand, agent_prob, trajectories)
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rewards.append(float(reward))
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return float(np.mean(rewards)) if rewards else 0.0
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def _select_adversarial_alpha(self, prices: np.ndarray) -> float:
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"""inner robust step: pick worst-case alpha from the ambiguity interval.
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when JAX is available and robust_rollouts==1 we use a vmapped pass over
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all K candidates in a single call (no Python loop, no market.act overhead).
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the JAX path approximates demand as the mixed closed-form d(p;theta) signal
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rather than running full trajectory sampling, which is accurate for the
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alpha-selection decision while being dramatically cheaper.
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when robust_rollouts>1 or JAX is unavailable we fall back to the sequential
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market.act() loop so behavior is identical to the original implementation.
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"""
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candidates = self._alpha_candidates()
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if len(candidates) == 1:
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return float(candidates[0])
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if _JAX_OK and self.robust_rollouts == 1:
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best_alpha, _ = select_adversarial_alpha_jax(
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candidates=candidates,
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prices=prices,
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human_params=self.market.human_params,
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agent_params=self.market.agent_params,
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noise_std=self.market.noise_std,
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baseline_prices=self.baseline_prices,
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lambda_coi=self.lambda_coi,
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info_value=self.info_value,
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reward_profit_weight=self.reward_profit_weight,
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rng_seed=self._global_step,
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)
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return best_alpha
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# fallback: full trajectory-based sequential evaluation
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evaluations = [
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(float(alpha), self._evaluate_candidate(float(alpha), prices))
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for alpha in candidates
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]
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best_alpha, _ = min(evaluations, key=lambda x: x[1])
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return best_alpha
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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._set_market_mix(self.nominal_alpha)
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self._limbo.reset()
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self._prices = np.random.uniform(*self.price_bounds, size=self.n_products)
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self._platform_stub.set_prices(self._prices)
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self._limbo.step()
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self._demand = self._limbo.step()
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self._initial_episode_prices = self._prices.copy()
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self._step_count = 0
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self._low_margin_streak = 0
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self._demand_history, self._price_history, self._revenue_history = [], [], []
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self._trajectories = list(getattr(self.market, "last_trajectories", []))
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self._last_agent_prob = float(self.nominal_alpha)
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self._last_alpha_adv = float(self.nominal_alpha)
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self._record_history()
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return self._get_obs(), {}
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def step(self, action):
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self._prices = self._decode_action(action)
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alpha_adv = self._select_adversarial_alpha(self._prices)
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self._global_step += 1 # always increment; JAX path may have already done so
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self._set_market_mix(alpha_adv)
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self._platform_stub.set_prices(self._prices)
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self._step_count += 1
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self._demand = self.market.act(self._prices)
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trajectories = list(self.market.last_trajectories)
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agent_prob = self._compute_agent_prob(trajectories)
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self._trajectories.extend(trajectories)
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self._last_agent_prob = float(agent_prob)
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self._last_alpha_adv = float(alpha_adv)
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reward, metrics = self._compute_reward(
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self._prices, self._demand, agent_prob, trajectories
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)
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self._record_history()
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# soft early termination when margin collapses for too long
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avg_margin = float(np.mean(self._prices) - self.price_bounds[0]) / max(
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float(np.mean(self._prices)), 1e-6
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)
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if avg_margin < self.margin_floor:
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self._low_margin_streak += 1
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else:
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self._low_margin_streak = 0
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margin_collapsed = self._low_margin_streak >= self.margin_floor_patience
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terminated = self._step_count >= self.max_steps or margin_collapsed
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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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"alpha_adv": float(alpha_adv),
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"alpha_nominal": float(self.nominal_alpha),
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"wasserstein_radius": float(self.robust_radius),
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"robust_rollouts": int(self.robust_rollouts),
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**metrics,
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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="capstone", 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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