Abstract. Most of multi-agent reinforcement learning algorithms aim to converge to a Nash equilibrium, but a Nash equilibrium does not necessarily mean a desirable result. On the o...
Multiagent learning literature has investigated iterated twoplayer games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such ...
Efficient Learning Equilibrium (ELE) is a natural solution concept for multi-agent encounters with incomplete information. It requires the learning algorithms themselves to be in ...
We consider a repeated Prisoner’s Dilemma game where two independent learning agents play against each other. We assume that the players can observe each others’ action but ar...
Learning to converge to an efficient, i.e., Pareto-optimal Nash equilibrium of the repeated game is an open problem in multiagent learning. Our goal is to facilitate the learning ...