DQN & PPO

reinforcement_learning/ppo/ppo_agent.py

@@ -39,10 +39,10 @@ class PPOAgent(Policy):
     # Decide on an action to take in the environment
 
     def act(self, state, eps=None):
-        if eps is not None:
-            # Epsilon-greedy action selection
-            if np.random.random() < eps:
-                return np.random.choice(np.arange(self.action_size))
+        # if eps is not None:
+        #     # Epsilon-greedy action selection
+        #     if np.random.random() < eps:
+        #         return np.random.choice(np.arange(self.action_size))
 
         self.policy.eval()
         with torch.no_grad():

run.py

@@ -29,7 +29,7 @@ VERBOSE = True
 # Checkpoint to use (remember to push it!)
 checkpoint = "./checkpoints/201105222046-5400.pth"  # 17.66104361971127 Depth 1
 checkpoint = "./checkpoints/201106073658-4400.pth"  # 15.64082361736683 Depth 1
-# checkpoint = "./checkpoints/201106090621-3300.pth"  # 15.64082361736683 Depth 1
+checkpoint = "./checkpoints/201106090621-4500.pth"  # 15.64082361736683 Depth 1
 
 # Use last action cache
 USE_ACTION_CACHE = False
@@ -53,8 +53,8 @@ state_size = tree_observation.observation_dim
 action_size = 5
 
 # Creates the policy. No GPU on evaluation server.
-# policy = DDDQNPolicy(state_size, action_size, Namespace(**{'use_gpu': False}), evaluation_mode=True)
-policy = PPOAgent(state_size, action_size, 10)
+policy = DDDQNPolicy(state_size, action_size, Namespace(**{'use_gpu': False}), evaluation_mode=True)
+# policy = PPOAgent(state_size, action_size, 10)
 policy.load(checkpoint)
 
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