Reinforcement Learning
Springer Verlag, Singapore
978-981-19-4932-6 (ISBN)
- Noch nicht erschienen - erscheint am 03.12.2024
- Versandkostenfrei innerhalb Deutschlands
- Auch auf Rechnung
- Verfügbarkeit in der Filiale vor Ort prüfen
- Artikel merken
This book is intended for readers who want to learn reinforcement learning systematically and applyreinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research.
Zhiqing Xiao obtained doctoral degree from Tsinghua University in 2016 and has more than 15 years in academic research and industrial practices on data-analytics and AI. He is the author of two AI bestsellers in Chinese: “Reinforcement Learning” and “Application of Neural Network and PyTorch” and published many academic papers. He also contributed to recent versions of the open-source software Gym.
Chapter 1. Introduction of Reinforcement Learning (RL).- Chapter 2. MDP: Markov Decision Process.- Chapter 3. Model-based Numerical Iteration.- Chapter 4. MC: Monte Carlo Learning.- Chapter 5. TD: Temporal Difference Learning.- Chapter 6. Function Approximation.- Chapter 7. PG: Policy Gradient.- Chapter 8. AC: Actor–Critic.- Chapter 9. DPG: Deterministic Policy Gradient.- Chapter 10. Maximum-Entropy RL.- Chapter 11. Policy-based Gradient-Free Algorithms.- Chapter 12. Distributional RL.- Chapter 13. Minimize Regret.- Chapter 14. Tree Search.- Chapter 15. More Agent–Environment Interfaces.- Chapter 16. Learn from Feedback and Imitation Learning.
Erscheint lt. Verlag | 7.10.2024 |
---|---|
Zusatzinfo | 60 Illustrations, color; 1 Illustrations, black and white; XLII, 593 p. 61 illus., 60 illus. in color. |
Verlagsort | Singapore |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
ISBN-10 | 981-19-4932-8 / 9811949328 |
ISBN-13 | 978-981-19-4932-6 / 9789811949326 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
aus dem Bereich