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

Genetic algorithms

Another class of black-box methods that has recently become a popular alternative to the value-based and policy gradient methods is genetic algorithms (GA). It is a large family of optimization methods with more than two decades of history behind it and a simple core idea of generating a population of N individuals, each of which is evaluated with the fitness function. Every individual means some combination of model parameters. Then, some subset of top performers is used to produce (called mutation) the next generation of the population. This process is repeated until we're satisfied with the performance of our population.

There are a lot of different methods in the GA family, for example, how to complete the mutation of the individuals for the next generation or how to rank the performers. Here, we will consider the simple GA method with some extensions, published in the paper by Felipe Petroski Such, Vashisht Madhavan, and others called Deep Neuroevolution...