update boltmann rl for Mesa 3

- updated boltzmann_rl for Mesa 3.0
- creating duplicate agents for some reason; need ot reset the unique_id iterator

rl/boltzmann_money/model.py

@@ -19,19 +19,16 @@
 # Import necessary libraries
 import numpy as np
 import seaborn as sns
-from mesa_models.boltzmann_wealth_model.model import (
-    BoltzmannWealthModel,
-    MoneyAgent,
-    compute_gini,
-)
+from mesa.examples.basic.boltzmann_wealth_model.agents import MoneyAgent
+from mesa.examples.basic.boltzmann_wealth_model.model import BoltzmannWealth
 
 NUM_AGENTS = 10
 
 
 # Define the agent class
 class MoneyAgentRL(MoneyAgent):
-    def __init__(self, unique_id, model):
-        super().__init__(unique_id, model)
+    def __init__(self, model):
+        super().__init__(model)
         self.wealth = np.random.randint(1, NUM_AGENTS)
 
     def move(self, action):
@@ -74,45 +71,46 @@ def take_money(self):
 
     def step(self):
         # Get the action for the agent
-        action = self.model.action_dict[self.unique_id]
+        # TODO: figure out why agents are being made twice
+        action = self.model.action_dict[self.unique_id - 11]
         # Move the agent based on the action
         self.move(action)
         # Take money from other agents in the same cell
         self.take_money()
 
 
 # Define the model class
-class BoltzmannWealthModelRL(BoltzmannWealthModel, gymnasium.Env):
-    def __init__(self, N, width, height):
-        super().__init__(N, width, height)
+class BoltzmannWealthModelRL(BoltzmannWealth, gymnasium.Env):
+    def __init__(self, n, width, height):
+        super().__init__(n, width, height)
         # Define the observation and action space for the RL model
         # The observation space is the wealth of each agent and their position
-        self.observation_space = gymnasium.spaces.Box(low=0, high=10 * N, shape=(N, 3))
+        self.observation_space = gymnasium.spaces.Box(low=0, high=10 * n, shape=(n, 3))
         # The action space is a MultiDiscrete space with 5 possible actions for each agent
-        self.action_space = gymnasium.spaces.MultiDiscrete([5] * N)
+        self.action_space = gymnasium.spaces.MultiDiscrete([5] * n)
         self.is_visualize = False
 
     def step(self, action):
         self.action_dict = action
         # Perform one step of the model
-        self.schedule.step()
+        self.agents.shuffle_do("step")
         # Collect data for visualization
         self.datacollector.collect(self)
         # Compute the new Gini coefficient
-        new_gini = compute_gini(self)
+        new_gini = self.compute_gini()
         # Compute the reward based on the change in Gini coefficient
         reward = self.calculate_reward(new_gini)
         self.prev_gini = new_gini
         # Get the observation for the RL model
         obs = self._get_obs()
-        if self.schedule.time > 5 * NUM_AGENTS:
+        if self.time > 5 * NUM_AGENTS:
             # Terminate the episode if the model has run for a certain number of timesteps
             done = True
             reward = -1
         elif new_gini < 0.1:
             # Terminate the episode if the Gini coefficient is below a certain threshold
             done = True
-            reward = 50 / self.schedule.time
+            reward = 50 / self.time
         else:
             done = False
         info = {}
@@ -142,20 +140,18 @@ def reset(self, *, seed=None, options=None):
             self.visualize()
         super().reset()
         self.grid = mesa.space.MultiGrid(self.grid.width, self.grid.height, True)
-        self.schedule = mesa.time.RandomActivation(self)
+        self.remove_all_agents()
         for i in range(self.num_agents):
             # Create MoneyAgentRL instances and add them to the schedule
-            a = MoneyAgentRL(i, self)
-            self.schedule.add(a)
+            a = MoneyAgentRL(self)
             x = self.random.randrange(self.grid.width)
             y = self.random.randrange(self.grid.height)
             self.grid.place_agent(a, (x, y))
-        self.prev_gini = compute_gini(self)
+        self.prev_gini = self.compute_gini()
         return self._get_obs(), {}
 
     def _get_obs(self):
         # The observation is the wealth of each agent and their position
-        obs = []
-        for a in self.schedule.agents:
-            obs.append([a.wealth, *list(a.pos)])
+        obs = [[a.wealth, *a.pos] for a in self.agents]
+        obs = np.array(obs)
         return np.array(obs)