Towards a Better Understanding of Learning with Multiagent Teams

no code implementations • 28 Jun 2023 • David Radke, Kate Larson, Tim Brecht, Kyle Tilbury

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 more effective than smaller ones.

Exploring the Benefits of Teams in Multiagent Learning

no code implementations • 4 May 2022 • 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.

Reinforcement Learning (RL)

The Importance of Credo in Multiagent Learning

no code implementations • 15 Apr 2022 • 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).

reinforcement-learning Reinforcement Learning (RL)