70 citations · 190 across the 27 of their papers we have counts for
32 papers
Towards a Better Understanding of Learning with Multiagent Teams
David Radke, Kate Larson, Tim Brecht +1
While it has long been recognized that a team of individual learning agents can be greater than the sum of its parts, recent work has shown that larger teams are not necessarily mo…
Deliberation and Voting in Approval-Based Multi-Winner Elections
Kanav Mehra, Nanda Kishore Sreenivas, Kate Larson
Citizen-focused democratic processes where participants deliberate on alternatives and then vote to make the final decision are increasingly popular today. While the computational…
Revealed Multi-Objective Utility Aggregation in Human Driving
Atrisha Sarkar, Kate Larson, Krzysztof Czarnecki
A central design problem in game theoretic analysis is the estimation of the players' utilities. In many real-world interactive situations of human decision making, including human…
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from…
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Zun Li, Marc Lanctot, Kevin R. McKee +7
Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…