From fe7dafed0a45dcb36df950a12abd25befaa543f3 Mon Sep 17 00:00:00 2001
From: Daniel Rosel <daniel@alves.world>
Date: Sun, 14 Dec 2025 17:53:48 +0100
Subject: [PATCH] high level defintion

---
 sim/rl/environment.py | 94 ++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 83 insertions(+), 11 deletions(-)

diff --git a/sim/rl/environment.py b/sim/rl/environment.py
index 803a4fd..a09438f 100644
--- a/sim/rl/environment.py
+++ b/sim/rl/environment.py
@@ -2,6 +2,7 @@ import gymnasium as gym
 from gymnasium import spaces
 import numpy as np
 from dataclasses import dataclass
+import pandas as pd
 
 # here when we say "learner" we mean the agent that is learning to optimize the pricing and "agent" is part of the envrionment where the agent is creating demand that that "learner" is processing"
 
@@ -9,17 +10,89 @@ from dataclasses import dataclass
 class BusinessLogicConstraints():
     max_price_adjustment : float = 0.3 # maximum adjustment of price
     system_max_price : float = 500.0 # maximum price allowed in the system
+    system_min_price : float = 1.0 # minimum price allowed in the system
     product_catelogue_size : int = 100 # number of products in the catalogue
 
 
+class CommercePlatform:
+    def __init__(self, product_catelogue_size: int, max_price: float, min_price: float):
+        self.product_catelogue_size = product_catelogue_size
+        self.max_price = max_price
+        self.min_price = min_price
+        self.simulation_history = []
+
+
+    def setup_true_demand(self,prices: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        human_price_elasticity = -1.5  # Example elasticity value
+        base_demand = 100  # Base demand for products
+        demand = base_demand * (prices / self.max_price) ** human_price_elasticity
+
+        agent_price_elasticity = -2.0  # Example elasticity value for agents
+        agent_base_demand = 150  # Base demand for agents
+        agent_demand = agent_base_demand * (prices / self.max_price) ** agent_price_elasticity
+
+        return demand + agent_demand, agent_demand
+
+
+    def compute_interaction_features(self, interaction_data: np.ndarray) -> dict:
+        df = pd.DataFrame(interaction_data)
+        return {
+            'mean_sale_price': df[df['action'] == 'purchase']['price'].mean(),
+        }
+
+    def run_pricing_simulation(self, prices: np.ndarray) -> np.ndarray:
+        # Simulate demand based on prices
+
+        observed_demand, demand_from_agents = self.setup_true_demand(prices)
+        true_demand = observed_demand - demand_from_agents
+
+        interaction_data = self.get_interaction_data()
+        interaction_features = self.compute_interaction_features(interaction_data)
+        demand_estimates = self.demand_estimate(interaction_data)
+        internal_error = np.abs(true_demand - demand_estimates) / (true_demand + 1e-6)
+
+        self.simulation_history.append(
+            {
+                'prices': prices,
+                'true_demand': true_demand,
+                'demand_estimates': demand_estimates,
+                'internal_error': internal_error,
+                'interaction_data': interaction_data,
+                'interaction_features': interaction_features
+            })
+        return np.array(interaction_data)
+
+    def get_interaction_data(self) -> np.ndarray:
+        # Simulate interaction data
+        interaction_data = []
+        return np.array(interaction_data)
+
+
+    def demand_estimate(self, interactions : np.ndarray) -> np.ndarray:
+        demand_estimates = np.random.rand(self.product_catelogue_size) * 100  # Dummy demand estimates
+        return demand_estimates
+
+
+
+
+
+
+
+
+
 class PHANTOMEnv(gym.Env):
     def __init__(self):
         super(PHANTOMEnv, self).__init__()
         self.constraints = BusinessLogicConstraints()
         self.action_space = spaces.Box(
             low=-self.constraints.max_price_adjustment, high=self.constraints.max_price_adjustment,
-            shape=(1,), dtype=np.float32) #  we allow teh learner to adjust price by some BusinessLogicConstraints factor
+            shape=(self.constraints.product_catelogue_size,), dtype=np.float32) #  we allow teh learner to adjust price by some BusinessLogicConstraints factor
         # Example for using image as input:
+        self.commerce_platform = CommercePlatform(
+            product_catelogue_size=self.constraints.product_catelogue_size,
+            max_price=self.constraints.system_max_price,
+            min_price=self.constraints.system_min_price
+        )
         self.observation_space = spaces.Dict({
             'elasticity': spaces.Dict({
                 'price': spaces.Box(low=0, high=self.constraints.system_max_price,
@@ -29,24 +102,23 @@ class PHANTOMEnv(gym.Env):
             })
         })
 
-    def reset(self, seed=None, options=None):
+    def reset(self, seed :int, options) -> tuple[dict, dict]:
         super().reset(seed=seed)
         # Initialize state
         self.state = {
-            'price': 100.0,  # base price
-            'demand': 0.0
+            'elasticity': {
+                'price': np.full((self.constraints.product_catelogue_size,), 100.0, dtype=np.float32),
+                'demand': np.full((self.constraints.product_catelogue_size,), 50.0, dtype=np.float32)
+            }
         }
         return self.state, {}
 
     def step(self, action):
-        # Apply action
-        price_adjustment = action[0]
-        new_price = self.state['price'] * (1 + price_adjustment)
-        self.state['price'] = new_price
+        self.state['price'] = np.clip(self.state['price'] * (1 + action),
+                            self.constraints.system_min_price,
+                            self.constraints.system_max_price)
+
 
-        # Simulate demand based on new price
-        demand = self.simulate_demand(new_price)
-        self.state['demand'] = demand
 
         # Calculate reward (e.g., revenue)
         reward = new_price * demand