Multi-Agent Reinforcement Learning
Foundations and Modern Approaches
$185.56
- Hardcover
394 pages
- Release Date
21 January 2025
Summary
The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.
Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy networ…
Book Details
| ISBN-13: | 9780262049375 |
|---|---|
| ISBN-10: | 0262049376 |
| Author: | Stefano V. Albrecht, Filippos Christianos |
| Publisher: | MIT Press Ltd |
| Imprint: | MIT Press |
| Format: | Hardcover |
| Number of Pages: | 394 |
| Release Date: | 21 January 2025 |
| Weight: | 567g |
| Dimensions: | 178mm x 114mm |
About The Author
Stefano V. Albrecht
Stefano V. Albrecht is Associate Professor in the School of Informatics at the University of Edinburgh, where he leads the Autonomous Agents Research Group. His research focuses on the development of machine learning algorithms for autonomous systems control and decision making, with a particular focus on deep reinforcement learning and multi-agent interaction.
Filippos Christianos is a research scientist in multi-agent deep reinforcement learning focusing on how MARL algorithms can be used efficiently and the author of multiple popular MARL-focused code libraries.
Lukas Schäfer is a researcher focusing on the development of more generalizable, robust, and sample-efficient decision making using deep reinforcement learning, with a particular focus on multi-agent reinforcement learning.
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