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

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

Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This 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 fi eld, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. 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 its 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 through 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 Spis treści: 1. What Is Reinforcement Learning?2. OpenAI Gym API and Gymnasium3. Deep Learning with PyTorch4. The Cross-Entropy Method5. Tabular Learning and the Bellman Equation6. Deep Q-Networks7. Higher-Level RL Libraries8. DQN Extensions 9. Ways to Speed Up RL10. Stocks Trading Using RL11. Policy Gradients12. (...) więcej Actor-Critic Methods - A2C and A3C13. The TextWorld Environment14. Web Navigation15. Continuous Action Space16. Trust Region Methods17. Black-Box Optimizations in RL18. Advanced Exploration19. Reinforcement Learning with Human Feedback20. AlphaGo Zero and MuZero21. RL in Discrete Optimization22. Multi-Agent RL O autorze: Maxim Lapan jest niezależnym badaczem z wieloletnim doświadczeniem zawodowym w dziedzinie programowania i architektury systemów. Gruntownie poznał takie zagadnienia jak duże zbiory danych, uczenie maszynowe i rozproszone systemy obliczeniowe o wysokiej wydajności. Obecnie zajmuje się zastosowaniami uczenia głębokiego, w tym głębokim przetwarzaniem języka naturalnego i głębokim uczeniem przez wzmacnianie. mniej