velocitatem/PHANTOM
1
0
Fork 0
mirror of https://github.com/velocitatem/PHANTOM.git synced 2026-05-31 08:33:36 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: introduced simple COI proxy

Browse Source
This commit is contained in:
Daniel Rosel
2026-01-31 12:06:48 +01:00
parent e7cb48e9cd
commit e8ef850089
1 changed files with 21 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
engine/wrapper.py
View File

@@ -3,6 +3,7 @@ from gymnasium import spaces
import numpy as np
from .engine import Limbo, MarketEngine, PricingEngine
from .lib.render import DashboardRenderer
from .lib.coi import compute_coi_proxy
class PHANTOM(gym.Env):
@@ -15,11 +16,13 @@ class PHANTOM(gym.Env):
N: int = 100,
price_bounds: tuple = (10.0, 150.0),
lambda_coi: float = 0.1,
coi_window: int = 10,
render_mode: str = None):
super().__init__()
self.n_products = n_products
self.price_bounds = price_bounds
self.lambda_coi = lambda_coi
self.coi_window = coi_window # K steps for rolling COI calculation
self.render_mode = render_mode
self.alpha = alpha
self.N = N
@@ -44,15 +47,22 @@ class PHANTOM(gym.Env):
self._price_history = []
self._revenue_history = []
self._renderer = None
self._initial_episode_prices = None # prices at episode start for COI calc
def _get_obs(self) -> dict:
demand_arr = np.array([self._demand.get(i, 0.0) for i in range(self.n_products)], dtype=np.float32)
return {"demand": demand_arr, "prices": self._prices.astype(np.float32)}
def _compute_coi_proxy(self):
return compute_coi_proxy(
self._price_history, self._demand_history, self._initial_episode_prices,
self._prices, self.price_bounds, self.alpha, self.coi_window
)
def _compute_reward(self, prices: np.ndarray, demand: dict) -> float:
revenue = np.sum(prices * np.array([demand.get(i, 0.0) for i in range(self.n_products)]))
# TODO: implement supra-competitive price punishment
return float(revenue)
coi_penalty = self.lambda_coi * self._compute_coi_proxy()
return float(revenue - coi_penalty)
def _record_history(self):
demand_arr = np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])
@@ -63,6 +73,7 @@ class PHANTOM(gym.Env):
def reset(self, seed=None, options=None):
super().reset(seed=seed)
self._prices = np.random.uniform(*self.price_bounds, size=self.n_products)
self._initial_episode_prices = self._prices.copy() # snapshot for COI calculation
self._demand = self.market.act(self._prices)
self._step_count = 0
self._demand_history, self._price_history, self._revenue_history = [], [], []
@@ -75,10 +86,17 @@ class PHANTOM(gym.Env):
self._step_count += 1
self._record_history()
coi_proxy = self._compute_coi_proxy()
reward = self._compute_reward(self._prices, self._demand)
terminated = self._step_count >= 100
return self._get_obs(), reward, terminated, False, {"step": self._step_count}
info = {
"step": self._step_count,
"coi_proxy": coi_proxy,
"coi_penalty": self.lambda_coi * coi_proxy,
"raw_revenue": np.sum(self._prices * np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])),
}
return self._get_obs(), reward, terminated, False, info
def _compute_elasticity(self) -> np.ndarray:
"""point elasticity: e = (dQ/dP) * (P/Q) via finite differences, clipped to [-5, 5]"""