# Learning Policies for Dynamic Coalition Formation in Multi-Robot Task Allocation

arXiv: https://arxiv.org/abs/2412.20397 · HTML: https://mlsanity.com/paper/2412.20397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2024.

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

- [Visual Swarm Navigation via Deep Reinforcement Learning and Evolutionary Hybrid Design](https://mlsanity.com/paper/2610.06400/) (2026)

## Abstract (arXiv)

We propose a decentralized, learning-based framework for dynamic coalition formation in Multi-Robot Task Allocation (MRTA). Our approach extends MAPPO by integrating spatial action maps, robot motion planning, intention sharing, and task allocation revision to enable effective and adaptive coalition formation. Extensive simulation studies confirm the effectiveness of our model, enabling each robot to rely solely on local information to learn timely revisions of task selections and form coalitions with other robots to complete collaborative tasks. The results also highlight the proposed framework's ability to handle large robot populations and adapt to scenarios with diverse task sets.