diff --git a/intermediate_source/reinforcement_q_learning.py b/intermediate_source/reinforcement_q_learning.py
index 78dc7e2fc6e..42bea7c3e9e 100644
--- a/intermediate_source/reinforcement_q_learning.py
+++ b/intermediate_source/reinforcement_q_learning.py
@@ -283,7 +283,7 @@ def select_action(state):
             # t.max(1) will return the largest column value of each row.
             # second column on max result is index of where max element was
             # found, so we pick action with the larger expected reward.
-            return policy_net(state).max(1)[1].view(1, 1)
+            return policy_net(state).max(1).indices.view(1, 1)
     else:
         return torch.tensor([[env.action_space.sample()]], device=device, dtype=torch.long)
 
@@ -360,12 +360,12 @@ def optimize_model():
 
     # Compute V(s_{t+1}) for all next states.
     # Expected values of actions for non_final_next_states are computed based
-    # on the "older" target_net; selecting their best reward with max(1)[0].
+    # on the "older" target_net; selecting their best reward with max(1).values
     # This is merged based on the mask, such that we'll have either the expected
     # state value or 0 in case the state was final.
     next_state_values = torch.zeros(BATCH_SIZE, device=device)
     with torch.no_grad():
-        next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0]
+        next_state_values[non_final_mask] = target_net(non_final_next_states).max(1).values
     # Compute the expected Q values
     expected_state_action_values = (next_state_values * GAMMA) + reward_batch