Task Allocation with Load Management in Multi-Agent Teams

TL;DR

This paper introduces a decentralized reinforcement learning framework for multi-agent teams that optimizes task allocation while managing load to prevent overloads and improve resilience.

Contribution

It presents a novel decision-making framework that incorporates load management into task allocation for multi-agent systems using reinforcement learning.

Findings

Load management improves team resilience.

Decentralized learning reduces resource wastage.

Agent importance measure predicts overload risks.

Abstract

In operations of multi-agent teams ranging from homogeneous robot swarms to heterogeneous human-autonomy teams, unexpected events might occur. While efficiency of operation for multi-agent task allocation problems is the primary objective, it is essential that the decision-making framework is intelligent enough to manage unexpected task load with limited resources. Otherwise, operation effectiveness would drastically plummet with overloaded agents facing unforeseen risks. In this work, we present a decision-making framework for multi-agent teams to learn task allocation with the consideration of load management through decentralized reinforcement learning, where idling is encouraged and unnecessary resource usage is avoided. We illustrate the effect of load management on team performance and explore agent behaviors in example scenarios. Furthermore, a measure of agent importance in collaboration is developed to infer team resilience when facing handling potential overload situations.