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 NLP chatbots Speed up RL models using algorithmic and engineering approaches New content on RL from human feedback (RLHF), MuZero, and transformers Book DescriptionReward yourself and take this journey into reinforcement learning 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. This edition includes a new chapter about using reinforcement learning as part of LLMs’ training procedures. 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 NLP chatbots, 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 chapters 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, D4PG, and others 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 forThis 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.