- Title: Factored Value Functions for Cooperative Multi-Agent Reinforcement Learning
- Speaker: Shimon Whiteson (University of Oxford)
- Time and date: 3pm to 4pm, September 16th, 2020 (Wednesday)
- Room: Virtual (Zoom)
The Game AI Research Group is glad to announce a (virtual) talk by Shimon Whiteson on Wednesday Sept 16 at 15:00.
All welcome (especially students), no pre-booking required
Abstract
Cooperative multi-agent reinforcement learning (MARL) considers how teams of agents can coordinate their behaviour to efficiently achieve common goals. A key challenge therein is how to learn cooperative policies in a centralised fashion that nonetheless can be executed in a decentralised fashion. In this talk, Shimon will discuss QMIX, a simple but powerful cooperative MARL algorithm that relies on factored value functions both to make learning efficient and to ensure decentralisability. Extensive results on the StarCraft Multi-Agent Challenge (SMAC), a benchmark Shimon has helped develop, confirm that QMIX outperforms alternative approaches, though further analysis shows that this is not always for the expected reasons.
Bio
Shimon Whiteson is a Professor of Computer Science at the University of Oxford and the Head of Research at Waymo UK. 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, a Google Faculty Research Award in 2017, and a JPMorgan Faculty Award in 2019.