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
