Deep Reinforcement Learning with TensorFlow 2.0

In this tutorial, I will showcase the upcoming TensorFlow 2.0 features through the lens of deep reinforcement learning (DRL) by implementing an advantage actor-critic (A2C) agent to solve the classic CartPole-v0 environment. While the goal is to showcase TensorFlow 2.0, I will do my best to make the DRL aspect approachable as well, including a brief overview of the field. In fact, since the main focus of the 2.0 release is making developers' lives easier, it's a great time to get into DRL with TensorFlow -- our full agent source is under 150 lines! The code is available as a notebook here and online on Google Colab here. As TensorFlow 2.0 is still in an experimental stage, I recommend installing it in a separate (virtual) environment.