#### Deep Reinforcement Learning Hands-On

##### By :

#### Deep Reinforcement Learning Hands-On

##### By:

#### 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

Free Chapter

What is Reinforcement Learning?

OpenAI Gym

Deep Learning with PyTorch

The Cross-Entropy Method

Tabular Learning and the Bellman Equation

Deep Q-Networks

DQN Extensions

Stocks Trading Using RL

Policy Gradients – An Alternative

The Actor-Critic Method

Asynchronous Advantage Actor-Critic

Chatbots Training with RL

Web Navigation

Continuous Action Space

Trust Regions – TRPO, PPO, and ACKTR

Black-Box Optimization in RL

Beyond Model-Free – Imagination

AlphaGo Zero

Index

Customer Reviews