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 network management. This course provides an introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The course first introduces the field's foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The course follows the new MIT Press textbook of the course lecturer available for free at www.marl-book.com.
Dr. Stefano V. Albrecht is Associate Professor in Artificial Intelligence in the School of Informatics, University of Edinburgh. He leads the Autonomous Agents Research Group (https://agents.inf.ed.ac.uk) which specialises in developing machine learning algorithms for autonomous systems control and decision making, with a particular focus on reinforcement learning and multi-agent interaction. In his roles as Royal Academy of Engineering and Royal Society Industrial Fellow, he actively develops industry applications in the areas of multi-robot warehouses with Dematic/KION, and autonomous driving with Five AI which completed one of the most extensive urban road trials of autonomous driving in London before being acquired by Bosch in 2022. Dr. Albrecht is affiliated with the Alan Turing Institute where he leads the Multi-Agent Systems theme. In 2022, he was nominated for the IJCAI Computers and Thought Award based on his research which introduced Stochastic Bayesian Games and optimal solution algorithms, which have since been applied in a range of domains. Previously, Dr. Albrecht was a postdoctoral fellow at the University of Texas at Austin working with Prof. Peter Stone. He obtained PhD and MSc degrees in Artificial Intelligence from the University of Edinburgh, and a BSc degree in Computer Science from Technical University of Darmstadt. He is co-author of the new MIT Press textbook "Multi-Agent Reinforcement Learning: Foundations and Modern Approaches" which is freely available at www.marl-book.com.
100€ - Student - In person at the IIIA
70€ - Student - Online live viewing
150€ - Academic Non-student - In person at the IIIA
100€ - Academic Non-student - Online live viewing
200€ - Non academic - In person at the IIIA
150€ - Non Academic - Online live viewing
Participation online only allows live viewing of the course. Access to the content afterwards will not be possible, except in special circumstances.