# Task Allocation with Load Management in Multi-Agent Teams

arXiv: https://arxiv.org/abs/2207.08279 · HTML: https://mlsanity.com/paper/2207.08279/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [QMDP-Net: Deep Learning for Planning under Partial Observability](https://mlsanity.com/paper/1703.06692/) (2017)
- [Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with Evolving and Unknown Robot Capabilities](https://mlsanity.com/paper/2003.03344/) (2020)
- [Resilient Task Allocation in Heterogeneous Multi-Robot Systems](https://mlsanity.com/paper/2009.04593/) (2020)
- [Resource allocation in dynamic multiagent systems](https://mlsanity.com/paper/2102.08317/) (2021)
- [Adaptive Workload Allocation for Multi-human Multi-robot Teams for Independent and Homogeneous Tasks](https://mlsanity.com/paper/2007.13897/) (2020)
- [A Multi-Agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing](https://mlsanity.com/paper/1509.06420/) (2015)
- [Multi-Agent Task Allocation in Complementary Teams: A Hunter and Gatherer Approach](https://mlsanity.com/paper/1912.05748/) (2019)

## Abstract (arXiv)

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.