reach +0!

reinforcement/deepQLearningAgents.py

@@ -17,6 +17,11 @@ class PacmanDeepQAgent(PacmanQAgent):
         self.epsilon_explore = 1.0
         self.epsilon0 = 0.4
         self.minimal_epsilon = 0.01
+        if model.kProductionMode:
+            self.epsilon_explore=0.01
+            self.epsilon0=0.01
+            self.minimal_epsilon=0.01
+            print("in production mode, epsilon set to 0.01")
         self.epsilon = self.epsilon0
         self.discount = 0.95
         self.update_frequency = 3

reinforcement/model.py

@@ -11,7 +11,7 @@ from torch import tensor, double, optim
 from torch.nn.functional import relu, mse_loss
 import torch
 
-
+kProductionMode=True
 class DeepQNetwork(Module):
     """
     A model that uses a Deep Q-value Network (DQN) to approximate Q(s,a) as part
@@ -26,10 +26,12 @@ class DeepQNetwork(Module):
         "*** YOUR CODE HERE ***"
         # Initialize layers
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        layer0_size=256
         layer1_size=512
         layer2_size=128
         layer3_size=64
-        self.fc1 = Linear(state_dim, layer1_size).to(self.device)
+        self.fc0 = Linear(state_dim, layer0_size).to(self.device)
+        self.fc1 = Linear(layer0_size, 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(layer3_size, action_dim).to(self.device)
@@ -82,7 +84,8 @@ class DeepQNetwork(Module):
         "*** YOUR CODE HERE ***"
         if states.device.type != self.device.type:
             states = states.to(self.device)
-        x = relu(self.fc1(states))
+        x = relu(self.fc0(states))
+        x = relu(self.fc1(x))
         x = relu(self.fc2(x))
         x = relu(self.fc3(x))
         Q_values = self.fc_out(x)
@@ -107,6 +110,9 @@ class DeepQNetwork(Module):
             None
         """
         "*** YOUR CODE HERE ***"
+        if kProductionMode:
+            print("in production mode, no update")
+            return
         self.optimizer.zero_grad()
         loss = self.get_loss(states, Q_target)
         loss.backward()
@@ -115,4 +121,4 @@ class DeepQNetwork(Module):
         # 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)
+            print("now lr is: ", self.optimizer.param_groups[0]['lr'],"update count", self.output_cnt)