Added loss function

main.py

@@ -64,8 +64,32 @@ class A2C(nn.Module):
         entropy = action_pd.entropy()
         return actions, action_log_probs, state_values, entropy
 
-    def get_losses(self):
-        pass
+    def get_losses(
+        self,
+        rewards: torch.Tensor,
+        action_log_probs: torch.Tensor,
+        value_preds: torch.Tensor,
+        entropy: torch.Tensor,
+        masks: torch.Tensor,
+        gamma: float,
+        ent_coef: float,
+        device: torch.device
+        ) -> tuple[torch.tensor, torch.tensor]:
+
+        advantages = torch.zeros(len(rewards), device=device)
+        #compute advantages 
+        #mask - 0 if end of episode
+        #gamma - coeffecient for value prediction
+        for t in range(len(rewards) - 1):
+            advantages[t] = (rewards[t] + masks[t] * gamma * (value_preds[t+1] - value_preds[t]))
+        
+        #calculate critic loss - MSE
+        critic_loss = advantages.pow(2).mean()
+
+        #calculate actor loss - give bonus for entropy to encourage exploration
+        actor_loss = -(advantages.detach() * action_log_probs).mean() - ent_coef * entropy.mean()
+
+        return (critic_loss, actor_loss)
 
     def update_params(self, critic_loss: torch.tensor, actor_loss: torch.tensor) -> None:
         self.critic_optim.zero_grad()
@@ -80,6 +104,7 @@ class A2C(nn.Module):
 n_episodes = 10
 
 #agent hyperparams
+gamma = 0.999
 ent_coef = 0.01 # coefficient for entropy bonus
 actor_lr = 0.001
 critic_lr = 0.005