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

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 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 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

1. What Is Reinforcement Learning?
2. OpenAI Gym
3. Deep Learning with PyTorch
4. The Cross-Entropy Method
5. Tabular Learning and the Bellman Equation
6. Deep Q-Networks
7. Higher-Level RL libraries
8. DQN Extensions
9. Ways to Speed up RL
10. Stocks Trading Using RL
11. Policy Gradients
12. The Actor-Critic Method
13. Asynchronous Advantage Actor-Critic
14. Training Chatbots with RL
15. The TextWorld environment
16. Web Navigation
17. Continuous Action Space
18. RL in Robotics
19. Trust Regions
20. Black-Box Optimization in RL
21. Advanced exploration
22. Beyond Model-Free
23. AlphaGo Zero
24. RL in Discrete Optimisation
25. Multi-agent RL

Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years' work expertise as a software developer and a systems architect lies from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data, machine learning, and large parallel distributed HPC and non-HPC systems, he is able to explain a number of complicated concepts in simple words and vivid examples. His current areas of interest are in practical applications of deep learning, such as deep natural language processing and deep reinforcement learning. Maxim lives in Moscow, Russian Federation, with his family.