DQN & PPO

reinforcement_learning/ppo/ppo_agent.py

@@ -39,6 +39,11 @@ 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))
+
         self.policy.eval()
         with torch.no_grad():
             output = self.policy(torch.from_numpy(state).float().unsqueeze(0).to(device))