diff --git a/intermediate_source/reinforcement_q_learning.py b/intermediate_source/reinforcement_q_learning.py
index 82424c80d7..0ae3ea9a90 100644
--- a/intermediate_source/reinforcement_q_learning.py
+++ b/intermediate_source/reinforcement_q_learning.py
@@ -9,6 +9,8 @@
 This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent
 on the CartPole-v1 task from `Gymnasium <https://gymnasium.farama.org>`__.
 
+You might find it helpful to read the original `Deep Q Learning (DQN) <https://arxiv.org/abs/1312.5602>`__ paper
+
 **Task**
 
 The agent has to decide between two actions - moving the cart left or
@@ -83,7 +85,11 @@
 plt.ion()
 
 # if GPU is to be used
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device(
+    "cuda" if torch.cuda.is_available() else
+    "mps" if torch.backends.mps.is_available() else
+    "cpu"
+)
 
 
 ######################################################################
@@ -397,7 +403,7 @@ def optimize_model():
 # can produce better results if convergence is not observed.
 #
 
-if torch.cuda.is_available():
+if torch.cuda.is_available() or torch.backends.mps.is_available():
     num_episodes = 600
 else:
     num_episodes = 50