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
Contributors
Preface
Other Books You May Enjoy
Index

Chapter 16. Black-Box Optimization in RL

In this chapter, we'll again change our perspective on Reinforcement Learning (RL) training and will switch to the so-called black-box optimizations, in particular the evolution strategies and genetic algorithms. These methods are at least a decade old, but recently several research studies were conducted, which showed the applicability of the methods to large-scale RL problems and their competitiveness with the value iteration and Policy Gradient (PG) methods.