Paths to Equilibrium in Normal-Form Games

arXiv:2403.18079v1 Announce Type: cross
Abstract: In multi-agent reinforcement learning (MARL), agents repeatedly interact across time and revise their strategies as new data arrives, producing a sequence of strategy profiles. This paper studies sequences of strategies satisfying a pairwise constraint inspired by policy updating in reinforcement learning, where an agent who is best responding in period $t$ does not switch its strategy in the next period $t+1$. This constraint merely requires that optimizing agents do not switch strategies, but does not …

abstract agent agents arxiv cs.ai cs.gt cs.lg data equilibrium form games multi-agent normal paper policy profiles reinforcement reinforcement learning strategies strategy studies type