diff --git a/tutorial-contents/405_DQN_Reinforcement_learning.py b/tutorial-contents/405_DQN_Reinforcement_learning.py
index 13e456a..3526cd8 100644
--- a/tutorial-contents/405_DQN_Reinforcement_learning.py
+++ b/tutorial-contents/405_DQN_Reinforcement_learning.py
@@ -58,7 +58,7 @@ class DQN(object):
         # input only one sample
         if np.random.uniform() < EPSILON:   # greedy
             actions_value = self.eval_net.forward(x)
-            action = torch.max(actions_value, 1)[1].data.numpy()[0, 0]     # return the argmax
+            action = torch.max(actions_value, 1)[1].data.numpy()[0]     # return the argmax
         else:   # random
             action = np.random.randint(0, N_ACTIONS)
         return action
@@ -87,7 +87,7 @@ class DQN(object):
         # q_eval w.r.t the action in experience
         q_eval = self.eval_net(b_s).gather(1, b_a)  # shape (batch, 1)
         q_next = self.target_net(b_s_).detach()     # detach from graph, don't backpropagate
-        q_target = b_r + GAMMA * q_next.max(1)[0]   # shape (batch, 1)
+        q_target = b_r + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1)   # shape (batch, 1)
         loss = self.loss_func(q_eval, q_target)
 
         self.optimizer.zero_grad()