changed to simple reference and set up agents for decision making

main.py

@@ -9,7 +9,7 @@ import matplotlib.pyplot as plt
 from tqdm import tqdm
 
 import gymnasium as gym
-from pettingzoo.mpe import simple_crypto_v3
+from pettingzoo.mpe import simple_reference_v3
 
 class A2C(nn.Module):
     def __init__(
@@ -73,10 +73,9 @@ class A2C(nn.Module):
         masks: torch.Tensor,
         gamma: float,
         ent_coef: float,
-        device: torch.device
         ) -> tuple[torch.tensor, torch.tensor]:
 
-        advantages = torch.zeros(len(rewards), device=device)
+        advantages = torch.zeros(len(rewards), device=self.device)
         #compute advantages 
         #mask - 0 if end of episode
         #gamma - coeffecient for value prediction
@@ -101,7 +100,7 @@ class A2C(nn.Module):
         self.actor_optim.step()
 
 #environment hyperparams
-n_episodes = 10
+n_episodes = 1
 
 #agent hyperparams
 gamma = 0.999
@@ -110,7 +109,7 @@ actor_lr = 0.001
 critic_lr = 0.005
 
 #environment setup
-env = simple_crypto_v3.parallel_env(render_mode="human")
+env = simple_reference_v3.parallel_env(render_mode="human")
 
 #obs_space
 #action_space
@@ -124,12 +123,60 @@ device = torch.device("cpu")
 #env_wrapper_stats = gym.wrappers.vector.RecordEpisodeStatistics(
 #    env, buffer_length=n_episodes
 #)
-observations, infos = env.reset()
-done = False
-while env.agents:
-    
-    actions = {agent: env.action_space(agent).sample() for agent in env.agents}
-    observations, rewards, terminations, truncations, infos = env.step(actions)
-    print(observations)
+#eve = A2C(n_features = env.observation_space("eve_0").shape[0], n_actions = env.action_space("eve_0").n, device = device, critic_lr = critic_lr, actor_lr = actor_lr)
+#bob = A2C(n_features = env.observation_space("bob_0").shape[0], n_actions = env.action_space("bob_0").n, device = device, critic_lr = critic_lr, actor_lr = actor_lr)
+#alice = A2C(n_features = env.observation_space("alice_0").shape[0], n_actions = env.action_space("alice_0").n, device = device, critic_lr = critic_lr, actor_lr = actor_lr)
+
+agent0 = A2C(n_features = env.observation_space("agent_0").shape[0], n_actions = env.action_space("agent_0").n, device = device, critic_lr = critic_lr, actor_lr = actor_lr)
+agent1 = A2C(n_features = env.observation_space("agent_1").shape[0], n_actions = env.action_space("agent_1").n, device = device, critic_lr = critic_lr, actor_lr = actor_lr)
+
+for _ in range(0, n_episodes):
+    observations, infos = env.reset()
+    agent_0_rewards = []
+    agent_0_probs = []
+    agent_0_pred = []
+    agent_0_ents = []
+    agent_0_mask = []
+
+    agent_1_rewards = []
+    agent_1_probs = []
+    agent_1_pred = []
+    agent_1_ents = []
+    agent_1_mask = []
+    while env.agents:
+        
+        actions = {}
+        #eve_action, eve_log_probs, eve_state_val, eve_ent = eve.select_action(observations["eve_0"])
+        #bob_action, bob_log_probs, bob_state_val, bob_ent = bob.select_action(observations["bob_0"])
+        #alice_action, alice_log_probs, alice_state_val, alice_ent = alice.select_action(observations["alice_0"])
+        #actions["eve_0"] = eve_action.item()
+        #actions["bob_0"] = bob_action.item()
+        #actions["alice_0"] = alice_action.item()
+        agent_0_action, agent_0_log_probs, agent_0_state_val, agent_0_ent = agent0.select_action(observations["agent_0"])
+        agent_1_action, agent_1_log_probs, agent_1_state_val, agent_1_ent = agent1.select_action(observations["agent_1"])
+        actions["agent_0"] = agent_0_action.item()
+        actions["agent_1"] = agent_1_action.item()
+        observations, rewards, terminations, truncations, infos = env.step(actions)
+        agent_0_rewards.append(rewards["agent_0"])
+        agent_0_probs.append(agent_0_log_probs)
+        agent_0_pred.append(agent_0_state_val)
+        agent_0_ents.append(agent_0_ent)
+        agent_0_mask.append( 1 if env.agents else 0)
+
+        agent_1_rewards.append(rewards["agent_1"])
+        agent_1_probs.append(agent_1_log_probs)
+        agent_1_pred.append(agent_1_state_val)
+        agent_1_ents.append(agent_1_ent)
+        agent_1_mask.append( 1 if env.agents else 0)
+        #eve_closs, eve_aloss = eve.get_losses([rewards["eve_0"]], eve_log_probs, eve_state_val, eve_ent, [1], gamma, ent_coef)
+        #print("Eve: Critic Loss: " + str(eve_closs.item()) + " Actor Loss: " + str(eve_aloss.item()))
+        #eve.update_params(eve_closs, eve_aloss)
+    agent_0_closs, agent_0_aloss = agent0.get_losses(torch.Tensor(agent_0_rewards), torch.Tensor(agent_0_probs), torch.Tensor(agent_0_pred), torch.Tensor(agent_0_ents), torch.Tensor(agent_0_mask), gamma, ent_coef)
+    print("Agent 0 loss: Critic: " + str(agent_0_closs.item()) + ", Actor: " + str(agent_0_aloss.item()))
+    agent0.update_params(agent_0_closs, agent_0_aloss)
+    agent_1_closs, agent_1_aloss = agent1.get_losses(torch.Tensor(agent_1_rewards), torch.Tensor(agent_1_probs), torch.Tensor(agent_1_pred), torch.Tensor(agent_1_ents), torch.Tensor(agent_1_mask), gamma, ent_coef)
+    print("Agent 1 loss: Critic: " + str(agent_1_closs.item()) + ", Actor: " + str(agent_1_aloss.item()))
+    agent1.update_params(agent_1_closs, agent_1_aloss)
+        
 
 env.close()