Empirically Evaluating Multiagent Learning Algorithms

TL;DR

This paper introduces MALT, a comprehensive testing suite for multiagent learning algorithms, enabling large-scale experiments that reveal insights into algorithm performance and challenge existing assumptions.

Contribution

The paper presents MALT, a new experimental framework that standardizes and scales the evaluation of multiagent learning algorithms, improving reproducibility and understanding.

Findings

Single-agent Q-learning outperformed more complex algorithms.

Many traditional assumptions about MAL algorithm performance were confirmed.

Surprising results challenge conventional wisdom in multiagent learning.

Abstract

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.