Deep Reinforcement Learning With TensorFlow 2.1 Roman Ring

In this tutorial, I will give an overview of the TensorFlow 2.x features through the lens of deep reinforcement learning (DRL) by implementing an advantage actor-critic (A2C) agent, solving the classic CartPole-v0 environment. While the goal is to showcase TensorFlow 2.x, I will do my best to make DRL approachable as well, including a birds-eye overview of the field. In fact, since the main focus of the 2.x release is making life easier for the developers, it's a great time to get into DRL with TensorFlow. For example, the source code for this blog post is under 150 lines, including comments! Code is available on GitHub here and as a notebook on Google Colab here.