Multi-agent machine learning : a reinforcement approach / Howard M. Schwartz
- Author
- Schwartz, Howard M.
- Published
- Hoboken, NJ : John Wiley & Sons Inc., [2014]
- Physical Description
- 1 online resource
Access Online
- John Wiley: ezaccess.libraries.psu.edu
- Summary
- "Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics. Framework for understanding a variety of methods and approaches in multi-agent machine learning. Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering"--
"Provide an in-depth coverage of multi-player, differential games and Gam theory"-- - Subject(s)
- ISBN
- 9781118884614 electronic bk.
1118884612 electronic bk.
9781118884478 (electronic bk.)
1118884477 (electronic bk.)
9781118884485
1118884485
9781118362082 (hardback)
111836208X (hardback)
9781322094762 (MyiLibrary)
1322094764 (MyiLibrary) - Note
- AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
- Bibliography Note
- Includes bibliographical references and index.
View MARC record | catkey: 13595762