Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF

Maxim Lapan delivers intuitive explanations and gradual 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 Speed up RL models using algorithmic and engineering approaches 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 reinforcement learning to the latest use cases, including the use of reinforcement learning with a wide variety of applications, including discrete optimization, game playing, stock trading, and web browser navigation.
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 reinforcement learning, its capabilities, and use cases. You’ll learn about key topics, such as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods.
If you want to learn about RL using a practical approach with real-world applications, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition is your ideal companion.
This book will equip you with both the practical know-how of RL and the theoretical foundation to understand and implement most modern RL papers. What you will learn Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs 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, and D4PG Implement RL algorithms using PyTorch and modern RL libraries Apply deep RL to real-world scenarios, from board games to stock trading Learn advanced exploration techniques for improved model performance Who this book is for
This book is ideal for machine learning engineers, software engineers and data scientists looking to apply deep reinforcement learning in practice. Both beginners and experienced practitioners will gain practical expertise in modern reinforcement learning techniques and their applications using PyTorch. 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