Talk details and link to meeting schedule below.
Cooperative Deep Multi-Agent Reinforcement Learning
Many real-world problems, such as network packet routing and the coordination of autonomous vehicles, are naturally modelled as cooperative multi-agent systems. In this talk, I overview some of the key challenges in developing reinforcement learning methods that can efficiently learn decentralised policies for such systems, including multi-agent credit assignment and representing and learning complex joint value functions. I also describe several multi-agent learning algorithms proposed in my lab to address these challenges. Finally, I present SMAC, a multi-agent testbed for multi-agent reinforcement learning in decentralised StarCraft II unit micromanagement, along with extensive empirical results obtained in this testbed.
Shimon Whiteson is a Professor of Computer Science at the University of Oxford and the Chief Scientist and Co-Founder of Latent Logic Ltd. His research focuses on deep reinforcement learning and learning from demonstration, with applications in robotics and video games. He completed his doctorate at the University of Texas at Austin in 2007. He spent eight years as an Assistant and then an Associate Professor at the University of Amsterdam before joining Oxford as an Associate Professor in 2015. He was awarded a Starting Grant from the European Research Council in 2014 and a Google Faculty Research Award in 2017.