Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games

Multiagent learning is a key problem in AI. In the presence of multi- ple Nash equilibria, even agents with non-conflicting interests may not be able to learn an optimal coordination policy. The problem is exac- cerbated if the agents do not know the game and independently receive noisy payoffs. So, multiagent reinforfcement learning involves two inter- related problems: identifying the game and learning to play. We provide a convergence proof, and show that the algorithm's parameters are easy to set to meet the convergence conditions.