update codes

codes/PPO/agent.py

@@ -29,13 +29,16 @@ class PPO:
         self.memory = PPOMemory(cfg.batch_size)
         self.loss = 0
 
-    def choose_action(self, observation):
-        state = torch.tensor([observation], dtype=torch.float).to(self.device)
+    def choose_action(self, state,continuous=False):
+        state = torch.tensor([state], dtype=torch.float).to(self.device)
         dist = self.actor(state)
         value = self.critic(state)
         action = dist.sample()
         probs = torch.squeeze(dist.log_prob(action)).item()
-        action = torch.squeeze(action).item()
+        if continuous:
+            action = torch.tanh(action)
+        else:
+            action = torch.squeeze(action).item()
         value = torch.squeeze(value).item()
         return action, probs, value