Publisher's Note: This edition from 2018 is outdated and not compatible with any of the most
recent updates to Python libraries. A new third edition updated for 2020 with six new chapters
that include multi-agent methods discrete optimization RL in robotics and advanced
exploration techniques is now available. This practical guide will teach you how deep learning
(DL) can be used to solve complex real-world problems. Key Features Explore deep
reinforcement learning (RL) from the first principles to the latest algorithms Evaluate
high-profile RL methods including value iteration deep Q-networks policy gradients TRPO
PPO DDPG D4PG evolution strategies and genetic algorithms Keep up with the very latest
industry developments including AI-driven chatbots Book Description 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. What you will learn
Understand the DL context of RL and implement complex DL models Learn the foundation of RL:
Markov decision processes Evaluate RL methods including Cross-entropy DQN Actor-Critic
TRPO PPO DDPG D4PG and others Discover how to deal with discrete and continuous action
spaces in various environments Defeat Atari arcade games using the value iteration method
Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to
play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including
AI-driven chatbots Who this book is for Some fluency in Python is assumed. Basic deep learning
(DL) approaches should be familiar to readers and some practical experience in DL will be
helpful. This book is an introduction to deep reinforcement learning (RL) and requires no
background in RL.
