Reinforcement Learning Repository at MSU

Topics: Distributed and Multi-Agent RL

When multiple agents simultaneously learn to cooperatively solve a problem, interactions among agents can defeat learning. The methods in multi-agent reinforcement learning seek to minimize such distractions and maximize cooperation towards a common goal. Some approaches include: modeling the agents' interaction with the environment, pre-specifying behaviors, and modeling team goals. Significant challenges are presented by how to represent roles within a team and the relationships between those roles.