refactoring training spc setup and benchmarking

engine/lib/tiers.py

@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Protocol
+
+import numpy as np
+
+
+class PolicyLike(Protocol):
+    def predict(self, obs: np.ndarray, deterministic: bool = True): ...
+
+
+class StaticPolicy:
+    def __init__(self, n_actions: int):
+        self._action = int(max(0, n_actions // 2))
+
+    def predict(self, obs: np.ndarray, deterministic: bool = True):
+        return self._action, None
+
+
+class SurgePolicy:
+    def __init__(
+        self,
+        n_actions: int,
+        n_products: int,
+        high_threshold: float = 60.0,
+        low_threshold: float = 30.0,
+    ):
+        self.n_actions = int(n_actions)
+        self.n_products = int(n_products)
+        self.mid = self.n_actions // 2
+        self.high_t = float(high_threshold)
+        self.low_t = float(low_threshold)
+
+    def predict(self, obs: np.ndarray, deterministic: bool = True):
+        obs_arr = np.asarray(obs, dtype=np.float32)
+        demand = obs_arr[: self.n_products]
+        demand_mean = float(np.mean(demand)) if demand.size > 0 else 0.0
+        if demand_mean >= self.high_t:
+            return min(self.mid + 2, self.n_actions - 1), None
+        if demand_mean <= self.low_t:
+            return max(self.mid - 2, 0), None
+        return self.mid, None
+
+
+@dataclass
+class LinearElasticityPolicy:
+    n_actions: int
+    n_products: int
+    price_low: float
+    price_high: float
+
+    def __post_init__(self):
+        self.n_actions = int(self.n_actions)
+        self.n_products = int(self.n_products)
+        self.price_low = float(self.price_low)
+        self.price_high = float(self.price_high)
+        self._target_price = 0.5 * (self.price_low + self.price_high)
+        self._action_scales = np.linspace(0.8, 1.2, self.n_actions)
+
+    def fit(self, env, warmup_steps: int = 800, seed: int = 42):
+        rng = np.random.default_rng(int(seed))
+        obs, _ = env.reset(seed=int(seed))
+        prices: list[float] = []
+        demands: list[float] = []
+
+        for _ in range(int(max(10, warmup_steps))):
+            action = int(rng.integers(0, self.n_actions))
+            obs, _, term, trunc, info = env.step(action)
+            done = bool(term or trunc)
+
+            p = np.asarray(info.get("prices", []), dtype=np.float32)
+            d = np.asarray(info.get("demand", []), dtype=np.float32)
+            if p.size > 0 and d.size > 0:
+                prices.append(float(np.mean(p)))
+                demands.append(float(np.mean(d)))
+
+            if done:
+                obs, _ = env.reset()
+
+        if len(prices) < 8:
+            self._target_price = 0.5 * (self.price_low + self.price_high)
+            return self
+
+        slope, intercept = np.polyfit(np.asarray(prices), np.asarray(demands), 1)
+        if slope < -1e-6:
+            p_star = -intercept / (2.0 * slope)
+            self._target_price = float(np.clip(p_star, self.price_low, self.price_high))
+        else:
+            self._target_price = 0.5 * (self.price_low + self.price_high)
+        return self
+
+    def predict(self, obs: np.ndarray, deterministic: bool = True):
+        obs_arr = np.asarray(obs, dtype=np.float32)
+        cur_prices = obs_arr[self.n_products : 2 * self.n_products]
+        cur_mean = (
+            float(np.mean(cur_prices)) if cur_prices.size > 0 else self._target_price
+        )
+        scale = self._target_price / max(cur_mean, 1e-6)
+        action = int(np.argmin(np.abs(self._action_scales - scale)))
+        return int(np.clip(action, 0, self.n_actions - 1)), None