replace model by policy

reinforcement_learning/reinforce.py

@@ -43,33 +43,33 @@ def forward(self, x):
         return F.softmax(action_scores)
 
 
-model = Policy()
-optimizer = optim.Adam(model.parameters(), lr=1e-2)
+policy = Policy()
+optimizer = optim.Adam(policy.parameters(), lr=1e-2)
 
 
 def select_action(state):
     state = torch.from_numpy(state).float().unsqueeze(0)
-    probs = model(Variable(state))
+    probs = policy(Variable(state))
     action = probs.multinomial()
-    model.saved_actions.append(action)
+    policy.saved_actions.append(action)
     return action.data
 
 
 def finish_episode():
     R = 0
     rewards = []
-    for r in model.rewards[::-1]:
+    for r in policy.rewards[::-1]:
         R = r + args.gamma * R
         rewards.insert(0, R)
     rewards = torch.Tensor(rewards)
     rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps)
-    for action, r in zip(model.saved_actions, rewards):
+    for action, r in zip(policy.saved_actions, rewards):
         action.reinforce(r)
     optimizer.zero_grad()
-    autograd.backward(model.saved_actions, [None for _ in model.saved_actions])
+    autograd.backward(policy.saved_actions, [None for _ in policy.saved_actions])
     optimizer.step()
-    del model.rewards[:]
-    del model.saved_actions[:]
+    del policy.rewards[:]
+    del policy.saved_actions[:]
 
 
 running_reward = 10
@@ -80,7 +80,7 @@ def finish_episode():
         state, reward, done, _ = env.step(action[0,0])
         if args.render:
             env.render()
-        model.rewards.append(reward)
+        policy.rewards.append(reward)
         if done:
             break