Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective

arXiv:2403.11345v1 Announce Type: new
Abstract: We address in this paper Reinforcement Learning (RL) among agents that are grouped into teams such that there is cooperation within each team but general-sum (non-zero sum) competition across different teams. To develop an RL method that provably achieves a Nash equilibrium, we focus on a linear-quadratic structure. Moreover, to tackle the non-stationarity induced by multi-agent interactions in the finite population setting, we consider the case where the number of agents within each team is …

abstract agents arxiv competition cs.ai cs.gt cs.lg cs.ma equilibrium focus general independent linear mean nash equilibrium paper perspective reinforcement reinforcement learning team teams type