chore: adding simulation logging with wandb

engine/wrapper.py

@@ -4,6 +4,7 @@ import numpy as np
 from .engine import Limbo, MarketEngine, PricingEngine
 from .lib.render import DashboardRenderer
 from .lib.coi import compute_coi_proxy
+from .lib.wrappers import EconomicMetricsWrapper
 
 
 class PHANTOM(gym.Env):
@@ -134,11 +135,43 @@ class PHANTOM(gym.Env):
 
 
 if __name__ == "__main__":
-    env = PHANTOM(n_products=15, alpha=0.3, N=100, render_mode="human")
-    obs, _ = env.reset()
-    for step in range(100):
-        action = env.action_space.sample()
-        obs, reward, term, trunc, info = env.step(action)
-        env.render()
-        if term: break
+    import wandb
+    from .lib import MetricsCallback
+
+    class RandomPolicy:
+        """Minimal SB3-compatible random policy for baseline testing."""
+        def __init__(self, env):
+            self.env = env
+            self.num_timesteps = 0
+
+        def learn(self, total_timesteps, callback=None):
+            callback.model = self
+            callback.num_timesteps = 0
+            callback.locals = {}
+            callback.on_training_start({}, {})
+
+            obs, _ = self.env.reset()
+            for step in range(total_timesteps):
+                action = self.env.action_space.sample()
+                obs, reward, term, trunc, info = self.env.step(action)
+                self.num_timesteps = step + 1
+                callback.num_timesteps = self.num_timesteps
+                callback.locals = {"infos": [info]}
+                callback.on_step()
+                if term or trunc:
+                    callback.on_rollout_end()
+                    obs, _ = self.env.reset()
+            return self
+
+        def predict(self, obs, **kwargs):
+            return self.env.action_space.sample(), None
+
+    wandb.init(project="phantom-pricing", config={"policy": "random", "alpha": 0.3})
+    env = EconomicMetricsWrapper(PHANTOM(n_products=15, alpha=0.3, render_mode=None))
+
+    model = RandomPolicy(env)
+    model.learn(total_timesteps=1000, callback=MetricsCallback())
+
+    print(f"Episode revenue: {env.episode_revenue:.1f}")
+    wandb.finish()
     env.close()