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

Evolution strategies

One subset of black-box optimization methods is called evolution strategies (ES), and it was inspired by the evolution process. With ES, the most successful individuals have the highest influence on the overall direction of the search. There are many different methods that fall into this class, and in this chapter, we will consider the approach taken by the OpenAI researchers Tim Salimans, Jonathan Ho, and others in their paper, Evolution Strategies as a Scalable Alternative to Reinforcement Learning [1], published in March 2017.

The underlying idea of ES methods is simple: on every iteration, we perform random perturbation of our current policy parameters and evaluate the resulting policy fitness function. Then, we adjust the policy weights proportionally to the relative fitness function value.

The concrete method used in the paper is called covariance matrix adaptation evolution strategy (CMA-ES), in which the perturbation performed is the random noise...