try to solve smallClassic

reinforcement/model.py

@@ -26,21 +26,25 @@ class DeepQNetwork(Module):
         "*** YOUR CODE HERE ***"
         # Initialize layers
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        layer1_size=300
-        layer2_size=300
-        # layer3_size=500
+        layer1_size=512
+        layer2_size=128
+        layer3_size=64
         self.fc1 = Linear(state_dim, layer1_size).to(self.device)
         self.fc2 = Linear(layer1_size, layer2_size).to(self.device)
-        # self.fc3 = Linear(layer2_size, layer3_size).to(self.device)
-        self.fc_out=  Linear(layer2_size, action_dim).to(self.device)
+        self.fc3 = Linear(layer2_size, layer3_size).to(self.device)
+        self.fc_out=  Linear(layer3_size, action_dim).to(self.device)
         
         # Set learning parameters
-        self.learning_rate = 0.1
-        self.numTrainingGames = 3800
+        self.learning_rate = 0.01
+        self.numTrainingGames = 5000
         self.batch_size = 128
 
         # Optimizer
         self.optimizer = optim.SGD(self.parameters(), lr=self.learning_rate)
+        # self.scheduler1 = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=27500, eta_min=self.learning_rate*0.25)  # Replace with CosineAnnealingLR
+        # self.scheduler2 = torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=0.9999)
+        self.output_step=500
+        self.output_cnt=0
 
         "**END CODE"""
         self.double()
@@ -80,7 +84,7 @@ class DeepQNetwork(Module):
             states = states.to(self.device)
         x = relu(self.fc1(states))
         x = relu(self.fc2(x))
-        # x = relu(self.fc3(x))
+        x = relu(self.fc3(x))
         Q_values = self.fc_out(x)
         return Q_values
 
@@ -106,4 +110,9 @@ class DeepQNetwork(Module):
         self.optimizer.zero_grad()
         loss = self.get_loss(states, Q_target)
         loss.backward()
-        self.optimizer.step()
+        self.optimizer.step()
+        # self.scheduler1.step()
+        # self.scheduler2.step()
+        self.output_cnt+=1
+        if self.output_cnt%self.output_step==0:
+            print("now lr is: ", self.optimizer.param_groups[0]['lr'],"update count", self.output_cnt)