Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition

A new edition of the bestselling guide to Deep Reinforcement Learning and how it can be 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 *2nd edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters * 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 and non-gradient methods * Discover ways to increase efficiency of RL methods both from theoretical and engineering perspective * Learn advanced exploration techniques including Noisy Networks, Pseudo-count and Network Distillation methods * Apply RL methods to cheap hardware robotics platform Book Description Deep Reinforcement Learning Hands-On 2nd edition is an updated and expanded version of the bestselling guide to the very latest RL tools and techniques. It provides you with an introduction to the fundamentals of Reinforcement Learning (RL) while providing you with the practical 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 emerging 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 Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning 2nd edition is your companion to navigating the exciting complexities of RL as it helps you attain expert hands-on experience and knowledge through practical real-world examples. What you will learn *Understand the DL context of RL and implement complex DL models * Learn the foundation of RL: Markov chain, Markov reward processes and Markov decision processes * Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others * Find cutting edge ways to speed up RL methods * 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 the Rubik's Cube * Gain insights into dealing with discrete and continuous action spaces in various environments * 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 * Discover advanced exploration techniques, including Noisy Networks and Network Distillation techniques 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.