Deep Reinforcement Learning Hands-On - Second Edition

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Higher-Level RL Libraries

In Chapter 6, Deep Q-Networks, we implemented the deep Q-network (DQN) model published by DeepMind in 2015 (https://deepmind.com/research/publications/playing-atari-deep-reinforcement-learning). This paper had a significant effect on the RL field by demonstrating that, despite common belief, it's possible to use nonlinear approximators in RL. This proof of concept stimulated great interest in the deep Q-learning field and in deep RL in general.

In this chapter, we will take another step towards practical RL by discussing higher-level RL libraries, which will allow you to build your code from higher-level blocks and focus on the details of the method that you are implementing. Most of the chapter will describe the ...