Buch, Englisch, 406 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 639 g
ISBN: 978-981-19-0637-4
Verlag: Springer Nature Singapore
These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Introduction.- 2. Tabular Value-Based Methods.- 3. Approximating the Value Function.- 4. Policy-Based Methods.- 5. Model-Based Methods.- 6. Two-Agent Reinforcement Learning.- 7. Multi-Agent Reinforcement Learning.- 8. Hierarchical Reinforcement Learning.- 9. Meta Learning.- 10. Further Developments.- A. Deep Reinforcement Learning Suites.- B. Deep Learning.- C. Mathematical Background.