diff --git a/tutorials/sequential_decision_making/DQN_and_DoubleDQN_example.ipynb b/tutorials/sequential_decision_making/DQN_and_DoubleDQN_example.ipynb
index 10d791dd..a6e87b4c 100644
--- a/tutorials/sequential_decision_making/DQN_and_DoubleDQN_example.ipynb
+++ b/tutorials/sequential_decision_making/DQN_and_DoubleDQN_example.ipynb
@@ -8,6 +8,8 @@
       "source": [
         "# Using DQN and Double DQN in Pearl with different neural network instantiations.\n",
         "\n",
+        "Here is a [better rendering](https://nbviewer.org/github/facebookresearch/Pearl/blob/main/tutorials/sequential_decision_making/DQN_and_DoubleDQN_example.ipynb) of this notebook on [nbviewer](https://nbviewer.org/).\n",
+        "\n",
         "- The purpose of this tutorial is twofold. First, it illustrates how users can use implementations of value based methods, for example, DQN and Double DQN, in Pearl. We use a simple Gym environment for illustration.\n",
         "\n",
         "- Second, it illustrates how users can instantiate a neural network (outside of a Pearl Agent) and pass it to different policy learners in Pearl. For both examples (DQN and Double DQN), we use an instantiation of `QValueNetworks` outside of the Pearl Agent. The default way right now is to instantiate a Q-value network inside the agent's policy learner.\n",