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

Board games


Most board games provide a setup that is different from an arcade scenario. The Atari game suite assumes that one player is making decisions in some environment with complex dynamics. By generalizing and learning from the outcome of their actions, the player improves their skills, increasing the final score.

In a board games setup, the rules of the game are usually quite simple and compact. What makes the game complicated is the amount of different positions on the board and the presence of an opponent with an unknown strategy, who tries to beat us in the game. The ability to observe the game state and explicit rules opens up the possibility to analyze the current position, which wasn't the case for Atari. The analysis means taking the current state of the game and evaluating all the possible moves that we can take, then choosing the best move as our action.

The simplest approach to evaluation is to iterate over the possible actions and recursively evaluate the position after the...