Task Allocation with Load Management in Multi-Agent Teams
This work addresses load management in multi-agent teams, which is crucial for maintaining operational effectiveness in applications like robot swarms or human-autonomy teams, but it appears incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of multi-agent task allocation under unexpected events by developing a decentralized reinforcement learning framework that incorporates load management to prevent agent overload, resulting in improved team performance and resilience as demonstrated in example scenarios.
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.