Empirically Evaluating Multiagent Reinforcement Learning Algorithms
This article makes two contributions. First, we present a platform for running and analyzing multiagent reinforcement learning experiments. Second, to demonstrate this platform we undertook and evaluated an empirical test of multiagent reinforcement learning algorithms from the literature, which to our knowledge is the largest such test ever conducted. We summarize some conclusions from our experiments, comparing algorithms on a variety of metrics including reward, regret, convergence to a Nash equilibrium and behavior in self play 1 .