diff --git a/pl_examples/domain_templates/reinforce_learn_Qnet.py b/pl_examples/domain_templates/reinforce_learn_Qnet.py
index 4d90faeb45..70726a7488 100644
--- a/pl_examples/domain_templates/reinforce_learn_Qnet.py
+++ b/pl_examples/domain_templates/reinforce_learn_Qnet.py
@@ -20,7 +20,7 @@ classic CartPole environment.
 To run the template, just run:
 `python reinforce_learn_Qnet.py`
 
-After ~1500 steps, you will see the total_reward hitting the max score of 200.
+After ~1500 steps, you will see the total_reward hitting the max score of 475+.
 Open up TensorBoard to see the metrics:
 
 `tensorboard --logdir default`
@@ -149,7 +149,7 @@ class Agent:
     """
     Base Agent class handling the interaction with the environment
 
-    >>> env = gym.make("CartPole-v0")
+    >>> env = gym.make("CartPole-v1")
     >>> buffer = ReplayBuffer(10)
     >>> Agent(env, buffer)  # doctest: +ELLIPSIS
     <...reinforce_learn_Qnet.Agent object at ...>
@@ -229,7 +229,7 @@ class Agent:
 class DQNLightning(pl.LightningModule):
     """ Basic DQN Model
 
-    >>> DQNLightning(env="CartPole-v0")  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    >>> DQNLightning(env="CartPole-v1")  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
     DQNLightning(
       (net): DQN(
         (net): Sequential(...)
@@ -393,7 +393,7 @@ class DQNLightning(pl.LightningModule):
         parser = parent_parser.add_argument_group("DQNLightning")
         parser.add_argument("--batch_size", type=int, default=16, help="size of the batches")
         parser.add_argument("--lr", type=float, default=1e-2, help="learning rate")
-        parser.add_argument("--env", type=str, default="CartPole-v0", help="gym environment tag")
+        parser.add_argument("--env", type=str, default="CartPole-v1", help="gym environment tag")
         parser.add_argument("--gamma", type=float, default=0.99, help="discount factor")
         parser.add_argument("--sync_rate", type=int, default=10, help="how many frames do we update the target network")
         parser.add_argument("--replay_size", type=int, default=1000, help="capacity of the replay buffer")