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feat: expanding market observation space

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This commit is contained in:
Daniel Rosel
2026-01-22 11:48:24 +01:00
parent 2b3d937be6
commit fa89347c4e
1 changed files with 24 additions and 7 deletions
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31
sim/rl/environment.py
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@@ -291,6 +291,7 @@ class CommercePlatform:
"mean_sale_price": 0.0,
"look_to_book": 0.0,
"coi": 0.0,
"expected_premium": 0.0,
}
purchases = interaction_df[interaction_df["eventName"] == "purchase_complete"]
@@ -330,7 +331,7 @@ class CommercePlatform:
}
def _session_feature_table(self, df: pd.DataFrame) -> pd.DataFrame:
# TODO: adapt this
"""Extract per-session behavioral features for separability analysis."""
if df.empty:
return pd.DataFrame()
g = df.groupby("session_id", sort=False)
@@ -341,8 +342,13 @@ class CommercePlatform:
views = g.apply(lambda x: int((x["eventName"] == "view_item_page").sum()), include_groups=False)
cart_adds = g.apply(lambda x: int((x["eventName"] == "add_item_to_cart").sum()), include_groups=False)
purchases = g.apply(lambda x: int((x["eventName"] == "purchase_complete").sum()), include_groups=False)
learn_more = g.apply(lambda x: int((x["eventName"] == "learn_more_about_item").sum()), include_groups=False)
conversion_rate = purchases / (views + 1e-6)
is_agent = g["actor"].apply(lambda s: bool((s == "agent").any()), include_groups=False)
# price sensitivity features
price_variance = g["price_offered"].var().fillna(0.0)
avg_price_seen = g["price_offered"].mean().fillna(0.0)
products_viewed = g["product_idx"].nunique()
return pd.DataFrame({
"session_duration_sec": session_duration.astype(float),
@@ -352,7 +358,11 @@ class CommercePlatform:
"item_views": views.astype(int),
"cart_adds": cart_adds.astype(int),
"purchases": purchases.astype(int),
"learn_more_clicks": learn_more.astype(int),
"conversion_rate": conversion_rate.astype(float),
"price_variance": price_variance.astype(float),
"avg_price_seen": avg_price_seen.astype(float),
"products_viewed": products_viewed.astype(int),
"is_agent": is_agent.astype(bool),
}).reset_index()
@@ -371,18 +381,25 @@ class PHANTOMEnv(gym.Env):
self.action_space = spaces.Box(low=-self.constraints.max_price_adjustment,
high=self.constraints.max_price_adjustment,
shape=(self.constraints.product_catalogue_size,), dtype=np.float32)
n_products = self.constraints.product_catalogue_size
self.observation_space = spaces.Dict({
"elasticity": spaces.Dict({
"price": spaces.Box(
low=np.full((self.constraints.product_catalogue_size,), self.constraints.system_min_price, dtype=np.float32),
high=np.full((self.constraints.product_catalogue_size,), self.constraints.system_max_price, dtype=np.float32),
low=np.full((n_products,), self.constraints.system_min_price, dtype=np.float32),
high=np.full((n_products,), self.constraints.system_max_price, dtype=np.float32),
dtype=np.float32),
"demand": spaces.Box(
low=np.zeros((self.constraints.product_catalogue_size,), dtype=np.float32),
high=np.full((self.constraints.product_catalogue_size,), 1e6, dtype=np.float32),
low=np.zeros((n_products,), dtype=np.float32),
high=np.full((n_products,), 1e6, dtype=np.float32),
dtype=np.float32),
})
# TODO: define more features that we compute from the interaction data
}),
"market": spaces.Dict({
"alpha_hat": spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32), # estimated agent share
"revenue_rate": spaces.Box(low=0.0, high=1e6, shape=(1,), dtype=np.float32), # recent revenue
"conversion_rate": spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
"price_volatility": spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
}),
"cost": spaces.Box(low=0.0, high=self.constraints.system_max_price, shape=(n_products,), dtype=np.float32),
})
self.commerce_platform = CommercePlatform(
product_catalogue_size=self.constraints.product_catalogue_size,