70 citations · 190 across the 27 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
Exploring the Benefits of Teams in Multiagent Learning
David Radke, Kate Larson, Tim Brecht
For problems requiring cooperation, many multiagent systems implement solutions among either individual agents or across an entire population towards a common goal. Multiagent team…
The Importance of Credo in Multiagent Learning
David Radke, Kate Larson, Tim Brecht
We propose a model for multi-objective optimization, a credo, for agents in a system that are configured into multiple groups (i.e., teams). Our model of credo regulates how agents…
Multi-Agent Advisor Q-Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and sl…