Empirically Evaluating Multiagent Learning Algorithms
This work addresses the problem of limited empirical understanding in multiagent learning for researchers, though it is incremental as it focuses on evaluation tools rather than new algorithms.
The authors tackled the lack of empirical evaluation for multiagent learning algorithms by developing the MultiAgent Learning Testbed (MALT), a tool suite enabling large-scale experiments, and found that single-agent Q-learning outperformed many modern algorithms in tests.
There exist many algorithms for learning how to play repeated bimatrix games. Most of these algorithms are justified in terms of some sort of theoretical guarantee. On the other hand, little is known about the empirical performance of these algorithms. Most such claims in the literature are based on small experiments, which has hampered understanding as well as the development of new multiagent learning (MAL) algorithms. We have developed a new suite of tools for running multiagent experiments: the MultiAgent Learning Testbed (MALT). These tools are designed to facilitate larger and more comprehensive experiments by removing the need to build one-off experimental code. MALT also provides baseline implementations of many MAL algorithms, hopefully eliminating or reducing differences between algorithm implementations and increasing the reproducibility of results. Using this test suite, we ran an experiment unprecedented in size. We analyzed the results according to a variety of performance metrics including reward, maxmin distance, regret, and several notions of equilibrium convergence. We confirmed several pieces of conventional wisdom, but also discovered some surprising results. For example, we found that single-agent $Q$-learning outperformed many more complicated and more modern MAL algorithms.