Adding model architecture for Reward, Value and Target Value

DPI/models.py

@@ -93,7 +93,7 @@ class ObservationDecoder(nn.Module):
         return out_dist
 
 
-class ActionDecoder(nn.Module):
+class Actor(nn.Module):
     def __init__(self, state_size, hidden_size, action_size, num_layers=5):
         super().__init__()
         self.state_size = state_size    
@@ -151,8 +151,24 @@ class ValueModel(nn.Module):
         value = self.value_model(state)
         value_dist = torch.distributions.independent.Independent(torch.distributions.Normal(value, 1), 1)
         return value_dist
-    
+
-        
+
+class RewardModel(nn.Module):
+    def __init__(self, state_size, hidden_size):
+        super().__init__()
+        self.reward_model = nn.Sequential(
+            nn.Linear(state_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, 1)
+        )
+
+    def forward(self, state):
+        reward = self.reward_model(state).squeeze(dim=1)
+        return reward
+
+
 class TransitionModel(nn.Module):
     def __init__(self, state_size, hidden_size, action_size, history_size):
         super().__init__()
@@ -194,8 +210,7 @@ class TransitionModel(nn.Module):
         prior = {"mean": state_prior_mean, "std": state_prior_std, "sample": sample_state_prior, "history": history, "distribution": state_prior_dist}
         return prior
     
-    def stack_states(self, states, dim=0):
+    def stack_states(self, states, dim=0):        
-        
         s = dict(
             mean = torch.stack([state['mean'] for state in states], dim=dim),
             std  = torch.stack([state['std'] for state in states], dim=dim),