Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (1)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (1)
By: Maxim Lapan

Overview of this book

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.
Table of Contents (28 chapters)
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Index

What this book covers

Chapter 1, What Is Reinforcement Learning?, contains an introduction to RL ideas and the main formal models.

Chapter 2, OpenAI Gym, introduces the practical aspects of RL, using the open source library Gym.

Chapter 3, Deep Learning with PyTorch, gives a quick overview of the PyTorch library.

Chapter 4, The Cross-Entropy Method, introduces one of the simplest methods in RL to give you an impression of RL methods and problems.

Chapter 5, Tabular Learning and the Bellman Equation, introduces the value-based family of RL methods.

Chapter 6, Deep Q-Networks, describes deep Q-networks (DQNs), an extension of the basic value-based methods, allowing us to solve a complicated environment.

Chapter 7, Higher-Level RL Libraries, describes the library PTAN, which we will use in the book to simplify the implementations of RL methods.

Chapter 8, DQN Extensions, gives a detailed overview of a modern extension to the DQN method, to improve its stability and convergence in complex environments.

Chapter 9, Ways to Speed up RL Methods, provides an overview of ways to make the execution of RL code faster.

Chapter 10, Stocks Trading Using RL, is the first practical project and focuses on applying the DQN method to stock trading.

Chapter 11, Policy Gradients—an Alternative, introduces another family of RL methods that is based on policy learning.

Chapter 12, The Actor-Critic Method, describes one of the most widely used methods in RL.

Chapter 13, Asynchronous Advantage Actor-Critic, extends the actor-critic method with parallel environment communication, which improves stability and convergence.

Chapter 14, Training Chatbots with RL, is the second project and shows how to apply RL methods to natural language processing problems.

Chapter 15, The TextWorld Environment, covers the application of RL methods to interactive fiction games.

Chapter 16, Web Navigation, is another long project that applies RL to web page navigation using the MiniWoB set of tasks.

Chapter 17, Continuous Action Space, describes the specifics of environments using continuous action spaces and various methods.

Chapter 18, RL in Robotics, covers the application of RL methods to robotics problems. In this chapter, I describe the process of building and training a small hardware robot with RL methods.

Chapter 19, Trust Regions – PPO, TRPO, ACKTR, and SAC, is yet another chapter about continuous action spaces describing the trust region set of methods.

Chapter 20, Black-Box Optimization in RL, shows another set of methods that don’t use gradients in their explicit form.

Chapter 21, Advanced Exploration, covers different approaches that can be used for better exploration of the environment.

Chapter 22, Beyond Model-Free – Imagination, introduces the model-based approach to RL and uses recent research results about imagination in RL.

Chapter 23, AlphaGo Zero, describes the AlphaGo Zero method and applies it to the game Connect 4.

Chapter 24, RL in Discrete Optimization, describes the application of RL methods to the domain of discrete optimization, using the Rubik’s Cube as an environment.

Chapter 25, Multi-agent RL, introduces a relatively new direction of RL methods for situations with multiple agents.