# Reinforcement Learning Based Heterogeneous Task Allocation Under Spatio-Temporal Environmental Uncertainty for Multi-Robot Teams

arXiv: https://arxiv.org/abs/2610.15237 · HTML: https://mlsanity.com/paper/2610.15237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 11 Oct 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/2110.14891/) (2021)
- [RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments](https://mlsanity.com/paper/2209.05738/) (2022)
- [Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints](https://mlsanity.com/paper/2005.13109/) (2020)
- [Robust Task Scheduling for Heterogeneous Robot Teams under Capability Uncertainty](https://mlsanity.com/paper/2106.12111/) (2021)
- [Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems](https://mlsanity.com/paper/2107.00144/) (2021)
- [Online Multi-Agent Pickup and Delivery with Task Deadlines](https://mlsanity.com/paper/2403.12377/) (2024)
- [An Approximation Algorithm for Risk-averse Submodular Optimization](https://mlsanity.com/paper/1807.09358/) (2018)
- [STT-CBS: A Conflict-Based Search Algorithm for Multi-Agent Path Finding with Stochastic Travel Times](https://mlsanity.com/paper/2004.08025/) (2020)
- [Consensus-based Fast and Energy-Efficient Multi-Robot Task Allocation](https://mlsanity.com/paper/2209.10513/) (2022)
- [An Interleaved Approach to Trait-Based Task Allocation and Scheduling](https://mlsanity.com/paper/2108.02773/) (2021)

## Abstract (arXiv)

Autonomous Surface Vehicles (ASVs) have been increasingly deployed to perform different kinds of maritime tasks in recent years, which motivates the need for a reliable task allocation policy that can adapt to heterogeneous tasks and maritime disturbances. In this work, we formulate a multi-robot task allocation problem in which a team of ASVs with heterogeneous capabilities needs to complete tasks of different types and appearance times under unknown stochastic spatio-temporal uncertainties from maritime disturbances. We propose a decentralized Reinforcement Learning (RL) based policy network that is trained to be robust towards uncertainty-induced observation deviations, and is able to perform individual decision making that leads to efficient execution behaviors toward heterogeneous task requirements. We evaluate the proposed policy and four baseline methods in simulated experiments with varied proportions of different tasks types, numbers of tasks, robot species and robots per species. Experiment results show that our proposed policy achieves the lowest makespan and can be deployed to address scenarios with 100 robots and 400 tasks.