English | 2020 | ISBN: ‎ 1838826998 | 827 pages | True PDF EPUB MOBI | 77.35 MB

New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more

Key Features

Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters

Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods

Apply RL methods to cheap hardware robotics platforms

Book Description

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 emeg 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.

What you will learn

Understand the deep learning context of RL and implement complex deep learning models

Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others

Build a practical hardware robot trained with RL methods for less than $100

Discover Microsoft's TextWorld environment, which is an interactive fiction games platform

Use discrete optimization in RL to solve a Rubik's Cube

Teach your agent to play Connect 4 using AlphaGo Zero

Explore the very latest deep RL research on topics including AI chatbots

Discover advanced exploration techniques, including noisy networks and network distillation techniques

Who this book is for

Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL

Table of Contents

What Is Reinforcement Learning?

OpenAI Gym

Deep Learning with PyTorch

The Cross-Entropy Method

Tabular Learning and the Bellman Equation

Deep Q-Networks

Higher-Level RL libraries

DQN Extensions

Ways to Speed up RL

Stocks Trading Using RL

Policy Gradients – an Alternative

The Actor-Critic Method

Asynchronous Advantage Actor-Critic

Training Chatbots with RL

The TextWorld environment

Web Navigation

Continuous Action Space

RL in Robotics

Trust Regions – PPO, TRPO, ACKTR, and SAC

Black-Box Optimization in RL

Advanced exploration

Beyond Model-Free – Imagination

AlphaGo Zero

RL in Discrete Optimisation

Multi-agent RL