Abstract

This paper experimentally evaluates multiagent learning algorithms playing repeated matrix games to maximize their cumulative return. Previous works assessed that Qlearning surpassed Nash-based multi-agent learning algorithms. Based on all-againstall repeated matrix game tournaments, this paper updates the state of the art of multiagent learning experiments. In a first stage, it shows that M-Qubed, S and bandit-based algorithms such as UCB are the best algorithms on general-sum games, Exp3 being the best on cooperative games and zero-sum games. In a second stage, our experiments show that two features forgetting the far past, and using recent history with states improve the learning algorithms. Finally, the best algorithms are two new algorithms, Qlearning and UCB enhanced with the two features, and M-Qubed.