diff --git a/intermediate_source/reinforcement_q_learning.py b/intermediate_source/reinforcement_q_learning.py
index d52c4f5fef1..cb9abc229c9 100644
--- a/intermediate_source/reinforcement_q_learning.py
+++ b/intermediate_source/reinforcement_q_learning.py
@@ -6,14 +6,14 @@
 
 
 This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent
-on the CartPole-v0 task from the `OpenAI Gym <https://www.gymlibrary.ml/>`__.
+on the CartPole-v0 task from the `OpenAI Gym <https://www.gymlibrary.dev/>`__.
 
 **Task**
 
 The agent has to decide between two actions - moving the cart left or
 right - so that the pole attached to it stays upright. You can find an
 official leaderboard with various algorithms and visualizations at the
-`Gym website <https://www.gymlibrary.ml/environments/classic_control/cart_pole>`__.
+`Gym website <https://www.gymlibrary.dev/environments/classic_control/cart_pole>`__.
 
 .. figure:: /_static/img/cartpole.gif
    :alt: cartpole
@@ -74,7 +74,7 @@
 import torchvision.transforms as T
 
 
-env = gym.make('CartPole-v0').unwrapped
+env = gym.make('CartPole-v0', new_step_api=True, render_mode='single_rgb_array').unwrapped
 
 # set up matplotlib
 is_ipython = 'inline' in matplotlib.get_backend()
@@ -254,7 +254,7 @@ def get_cart_location(screen_width):
 def get_screen():
     # Returned screen requested by gym is 400x600x3, but is sometimes larger
     # such as 800x1200x3. Transpose it into torch order (CHW).
-    screen = env.render(mode='rgb_array').transpose((2, 0, 1))
+    screen = env.render().transpose((2, 0, 1))
     # Cart is in the lower half, so strip off the top and bottom of the screen
     _, screen_height, screen_width = screen.shape
     screen = screen[:, int(screen_height*0.4):int(screen_height * 0.8)]
@@ -461,7 +461,7 @@ def optimize_model():
     for t in count():
         # Select and perform an action
         action = select_action(state)
-        _, reward, done, _ = env.step(action.item())
+        _, reward, done, _, _ = env.step(action.item())
         reward = torch.tensor([reward], device=device)
 
         # Observe new state