Deep Reinforcement Learning Hands-On (ebook) Chorzów

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. Spis treści: 1. What Is Reinforcement Learning?2. OpenAI Gym3. Deep Learning with PyTorch4. The Cross-Entropy Method5. Tabular Learning and the Bellman Equation 6. Deep Q-Networks7. Higher-Level RL libraries8. DQN Extensions9. Ways to Speed up RL10. Stocks Trading Using RL11. Policy Gradients – an Alternative12. The Actor-Critic Method13. Asynchronous Advantage Actor-Critic14. Training Chatbots with RL15. The TextWorld environment16. Web Navigation17. Continuous Action Space18. RL in Robotics19. Trust Regions – PPO, TRPO, ACKTR, and SAC20. Black-Box Optimization in RL21. Advanced exploration22. Beyond Model-Free – Imagination23. AlphaGo Zero24. RL in Discrete Optimisation25. 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.