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.

Multiagent learning is a key problem in AI. In the presence of multiple Nash equilibria, even agents with non-conflicting interests may not be able to learn an optimal coordination policy. The problem is exaccerbated if the agents do not know the game and independently receive noisy payoffs. So, multiagent reinforfcement learning involves two interrelated problems: identifying the game and learning to play.

Multiagent learning is a key problem in AI. In the presence of multiple Nash equilibria, even agents with non-conflicting interests may not be able to learn an optimal coordination policy. The problem is exaccerbated if the agents do not know the game and independently receive noisy payoffs. So, multiagent reinforfcement learning involves two interrelated problems: identifying the game and learning to play.

Multiagent learning is a key problem in AI. In the presence of multiple Nashequilibria, even agents with non-conflicting interests may not be able to learn an optimal coordination policy. The problem is exaccerbated ifthe agents do not know the game and independently receive noisy payoffs. So, multiagent reinforfcement learning involves two interrelated problems:identifying the game and learning to play.