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formlating the reward simply

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Daniel Rosel
2025-12-14 17:59:34 +01:00
parent fe7dafed0a
commit c5caee21b1
1 changed files with 22 additions and 7 deletions
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29
sim/rl/environment.py
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@@ -40,7 +40,7 @@ class CommercePlatform:
'mean_sale_price': df[df['action'] == 'purchase']['price'].mean(), 'mean_sale_price': df[df['action'] == 'purchase']['price'].mean(),
} }
def run_pricing_simulation(self, prices: np.ndarray) -> np.ndarray: def run_pricing_simulation(self, prices: np.ndarray) -> dict:
# Simulate demand based on prices # Simulate demand based on prices
observed_demand, demand_from_agents = self.setup_true_demand(prices) observed_demand, demand_from_agents = self.setup_true_demand(prices)
@@ -51,16 +51,17 @@ class CommercePlatform:
demand_estimates = self.demand_estimate(interaction_data) demand_estimates = self.demand_estimate(interaction_data)
internal_error = np.abs(true_demand - demand_estimates) / (true_demand + 1e-6) internal_error = np.abs(true_demand - demand_estimates) / (true_demand + 1e-6)
self.simulation_history.append(
{ summary = {
'prices': prices, 'prices': prices,
'true_demand': true_demand, 'true_demand': true_demand,
'demand_estimates': demand_estimates, 'demand_estimates': demand_estimates,
'internal_error': internal_error, 'internal_error': internal_error,
'interaction_data': interaction_data, 'interaction_data': interaction_data,
'interaction_features': interaction_features 'interaction_features': interaction_features
}) }
return np.array(interaction_data) self.simulation_history.append(summary)
return summary
def get_interaction_data(self) -> np.ndarray: def get_interaction_data(self) -> np.ndarray:
# Simulate interaction data # Simulate interaction data
@@ -118,10 +119,24 @@ class PHANTOMEnv(gym.Env):
self.constraints.system_min_price, self.constraints.system_min_price,
self.constraints.system_max_price) self.constraints.system_max_price)
result = self.commerce_platform.run_pricing_simulation(self.state['price'])
history = self.commerce_platform.simulation_history
self.state['demand'] = result['demand_estimates']
reward = sum(
self.state['price'] * self.state['demand'],
# performance historically, to take into account business kpi trends (using features from interaction data)
sum(
[-0.05 * i * history[-1]['internal_error'] for i in range(1, len(history))],
) if len(history) > 1 else 0,
sum(
[0.1 * history[-1]['interaction_features']['mean_sale_price'] - 0.1 * history[i]['interaction_features']['mean_sale_price'] for i in range(len(history)-1)],
) if len(history) > 1 else 0
)
# Calculate reward (e.g., revenue)
reward = new_price * demand
# Check if episode is done # Check if episode is done
done = self.state['price'] <= 0.0 or self.state['demand'] <= 0.0 done = self.state['price'] <= 0.0 or self.state['demand'] <= 0.0