# Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays

arXiv: https://arxiv.org/abs/2502.16079 · HTML: https://mlsanity.com/paper/2502.16079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [ChainerRL: A Deep Reinforcement Learning Library](https://mlsanity.com/paper/1912.03905/) (2019)
- [RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments](https://mlsanity.com/paper/2209.05738/) (2022)
- [DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments](https://mlsanity.com/paper/2209.02865/) (2022)
- [Decentralized Task and Path Planning for Multi-Robot Systems](https://mlsanity.com/paper/2011.10034/) (2020)

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers](https://mlsanity.com/paper/2607.05683/) (2026)

## Abstract (arXiv)

Efficient task allocation among multiple robots is crucial for optimizing productivity in modern warehouses, particularly in response to the increasing demands of online order fulfillment. This paper addresses the real-time multi-robot task allocation (MRTA) problem in dynamic warehouse environments, where tasks emerge with specified start and end locations. The objective is to minimize both the total travel distance of robots and delays in task completion, while also considering practical constraints such as battery management and collision avoidance. We introduce MRTAgent, a dual-agent Reinforcement Learning (RL) framework inspired by self-play, designed to optimize task assignments and robot selection to ensure timely task execution. For safe navigation, a modified linear quadratic controller (LQR) approach is employed. To the best of our knowledge, MRTAgent is the first framework to address all critical aspects of practical MRTA problems while supporting continuous robot movements.