Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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Chapter 7. DQN Extensions

In the previous chapter, we implemented the Deep Q-Network (DQN) model published by DeepMind in 2015. This paper had a significant effect on the Reinforcement Learning (RL) field by demonstrating that, despite common belief, it's possible to use nonlinear approximators in RL. This proof of concept stimulated large interest in the deep Q-learning field in particular and in deep RL in general.

Since then, many improvements have been proposed, along with tweaks to the basic architecture, which significantly improve convergence, stability and sample efficiency of the basic DQN invented by DeepMind. In this chapter, we'll take a deeper look at some of those ideas. Very conveniently, in October 2017, DeepMind published a paper called Rainbow: Combining Improvements in Deep Reinforcement Learning ([1] Hessel and others, 2017), which presented the seven most important improvements to DQN, some of which were invented in 2015, but some of which are very recent. In this paper...