diff --git a/tutorial-contents/405_DQN_Reinforcement_learning.py b/tutorial-contents/405_DQN_Reinforcement_learning.py
index 20fa80c..f81c6b8 100644
--- a/tutorial-contents/405_DQN_Reinforcement_learning.py
+++ b/tutorial-contents/405_DQN_Reinforcement_learning.py
@@ -26,14 +26,15 @@ env = gym.make('CartPole-v0')
 env = env.unwrapped
 N_ACTIONS = env.action_space.n
 N_STATES = env.observation_space.shape[0]
+ENV_A_SHAPE = 0 if isinstance(env.action_space.sample(), int) else env.action_space.sample().shape     # to confirm the shape
 
 
 class Net(nn.Module):
     def __init__(self, ):
         super(Net, self).__init__()
-        self.fc1 = nn.Linear(N_STATES, 10)
+        self.fc1 = nn.Linear(N_STATES, 50)
         self.fc1.weight.data.normal_(0, 0.1)   # initialization
-        self.out = nn.Linear(10, N_ACTIONS)
+        self.out = nn.Linear(50, N_ACTIONS)
         self.out.weight.data.normal_(0, 0.1)   # initialization
 
     def forward(self, x):
@@ -58,9 +59,11 @@ 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()
+            action = action[0, 0] if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)  # return the argmax index
         else:   # random
             action = np.random.randint(0, N_ACTIONS)
+            action = action if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)
         return action
 
     def store_transition(self, s, a, r, s_):