update

tutorial-contents/405_DQN_Reinforcement_learning.py

@@ -46,8 +46,8 @@ class DQN(object):
     def __init__(self):
         self.eval_net, self.target_net = Net(), Net()
 
-        self.learn_step_counter = 0     # for target updateing
-        self.memory_counter = 0         # for storing memory
+        self.learn_step_counter = 0                                     # for target updating
+        self.memory_counter = 0                                         # for storing memory
         self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))     # initialize memory
         self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)
         self.loss_func = nn.MSELoss()
@@ -100,7 +100,6 @@ for i_episode in range(400):
     ep_r = 0
     while True:
         env.render()
-
         a = dqn.choose_action(s)
 
         # take action
@@ -112,7 +111,6 @@ for i_episode in range(400):
         r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
         r = r1 + r2
 
-        # store experience
         dqn.store_transition(s, a, r, s_)
 
         ep_r += r
@@ -120,10 +118,8 @@ for i_episode in range(400):
             dqn.learn()
             if done:
                 print('Ep: ', i_episode,
-                      '| Ep_r: ', round(ep_r, 2),
-                      )
+                      '| Ep_r: ', round(ep_r, 2))
 
         if done:
             break
-
         s = s_