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Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methods
Purchase of the print or Kindle book includes a free PDF eBook
Key Features:
- Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation
- Develop deep RL models, improve their stability, and efficiently solve complex environments
- New content on RL from human feedback (RLHF), MuZero, and transformers
Book Description:
Reward yourself and take this journey into RL with the third edition of Deep Reinforcement Learning Hands-On. The book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the field, this deep reinforcement learning book will equip you with the practical know-how of RL and the theoretical foundation to understand and implement most modern RL papers.
The book retains its strengths by providing concise and easy-to-follow explanations. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods.
If you want to learn about RL using a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition is your ideal companion
What You Will Learn:
- Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs
- Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG
- Implement RL algorithms using PyTorch and modern RL libraries
- Build and train deep Q-networks to solve complex tasks in Atari environments
- Speed up RL models using algorithmic and engineering approaches
- Leverage advanced techniques like proximal policy optimization (PPO) for more stable training
Who this book is for:
This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it's also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and finance
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
- Actor-Critic Methods - A2C and A3C
- The TextWorld Environment
- Web Navigation
- Continuous Action Space
- Trust Regions - PPO, TRPO, ACKTR, and SAC
- Black-Box Optimization in RL
- Advanced Exploration
- RL with Human Feedback
- MuZero
- RL in Discrete Optimization
- Multi-agent RL
- RL in Robotics
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
- 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
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
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
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.