Generalization in Cooperative Multi-Agent Systems

TL;DR

This paper explores the theoretical foundations of combinatorial generalization in cooperative multi-agent systems, providing bounds and insights that can improve the deployability of such systems across diverse scenarios.

Contribution

It introduces a theoretical framework for understanding and bounding generalization in multi-agent systems based on agent capabilities and reward dependencies.

Findings

Generalization bounds depend linearly on agent capabilities.

Extends bounds to Lipschitz and arbitrary reward dependencies.

Empirical analysis highlights key algorithmic considerations for generalization.

Abstract

Collective intelligence is a fundamental trait shared by several species of living organisms. It has allowed them to thrive in the diverse environmental conditions that exist on our planet. From simple organisations in an ant colony to complex systems in human groups, collective intelligence is vital for solving complex survival tasks. As is commonly observed, such natural systems are flexible to changes in their structure. Specifically, they exhibit a high degree of generalization when the abilities or the total number of agents changes within a system. We term this phenomenon as Combinatorial Generalization (CG). CG is a highly desirable trait for autonomous systems as it can increase their utility and deployability across a wide range of applications. While recent works addressing specific aspects of CG have shown impressive results on complex domains, they provide no performance guarantees when generalizing towards novel situations. In this work, we shed light on the theoretical underpinnings of CG for cooperative multi-agent systems (MAS). Specifically, we study generalization bounds under a linear dependence of the underlying dynamics on the agent capabilities, which can be seen as a generalization of Successor Features to MAS. We then extend the results first for Lipschitz and then arbitrary dependence of rewards on team capabilities. Finally, empirical analysis on various domains using the framework of multi-agent reinforcement learning highlights important desiderata for multi-agent algorithms towards ensuring CG.