Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning. (arXiv:2209.14344v1 [cs.LG])

Equilibrium selection in multi-agent games refers to the problem of selecting
a Pareto-optimal equilibrium. It has been shown that many state-of-the-art
multi-agent reinforcement learning (MARL) algorithms are prone to converging to
Pareto-dominated equilibria due to the uncertainty each agent has about the
policy of the other agents during training. To address suboptimal equilibrium
selection, we propose Pareto-AC (PAC), an actor-critic algorithm that utilises
a simple principle of no-conflict games (a superset of cooperative games with
identical rewards): each agent can …

actor-critic arxiv equilibrium reinforcement reinforcement learning