Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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Index

DQN Extensions

Since DeepMind published its paper on the deep Q-network (DQN) model (https://deepmind.com/research/publications/playing-atari-deep-reinforcement-learning) in 2015, many improvements have been proposed, along with tweaks to the basic architecture, which, significantly, have improved the convergence, stability, and sample efficiency of DeepMind's basic DQN. In this chapter, we will 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 were invented in 2015, but others were very recent. In this paper, state-of-the-art results on the Atari games suite were reached, just by combining those seven methods. This chapter will go through all those methods. We will analyze the ideas behind them, alongside how they can be implemented and compared to the...