Title: DelAC: A Multi-agent Reinforcement Learning of Team-Symmetric Stochastic Games
Primary area: reinforcement learning
Keywords: symmetric games, team games, reinforcement learning, multi-agent, Nash equilibrium, actor-critic

Abstract: In this paper we study team-symmetric games with two teams.  Players within
	the same team have common payoff functions.  We show that team-symmetric games
	always have a team-symmetric Nash equilibrium.  We develop and solve a linear
	complementarity problem of team-symmetric Nash equilibria.  We propose an actor-critic
	based multi-agent reinforcement learning algorithm for team-symmetric games.
	Through simulations, we show that this multi-agent reinforcement learning algorithm
	performs much better than many existing algorithms.

TL;DR: We analyze symmetric Nash equilibria of a team-symmetric game and propose an actor-critic based multi-agent reinforcement learning scheme for it.