# Multi-agent assignment via state augmented reinforcement learning

arXiv: https://arxiv.org/abs/2406.01782 · HTML: https://mlsanity.com/paper/2406.01782/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Constrained Reinforcement Learning Has Zero Duality Gap](https://mlsanity.com/paper/1910.13393/) (2019)
- [Uncertainty-Aware Reinforcement Learning for Collision Avoidance](https://mlsanity.com/paper/1702.01182/) (2017)
- [Safe Policies for Reinforcement Learning via Primal-Dual Methods](https://mlsanity.com/paper/1911.09101/) (2019)
- [Reinforcement Learning with Convex Constraints](https://mlsanity.com/paper/1906.09323/) (2019)
- [State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards](https://mlsanity.com/paper/2102.11941/) (2021)
- [Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration](https://mlsanity.com/paper/2006.12749/) (2020)
- [A Distributed Version of the Hungarian Method for Multi-Robot Assignment](https://mlsanity.com/paper/1805.08712/) (2018)

## Built on by (most cited first, 3 of 3)

- [Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics](https://mlsanity.com/paper/2605.30461/) (2026)
- [Coordination Graphs for Constrained Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2606.02337/) (2026)
- [Learning Infinite-Horizon Average-Reward CMDPs via State Augmentation](https://mlsanity.com/paper/2609.39093/) (2026)

## Abstract (arXiv)

We address the conflicting requirements of a multi-agent assignment problem through constrained reinforcement learning, emphasizing the inadequacy of standard regularization techniques for this purpose. Instead, we recur to a state augmentation approach in which the oscillation of dual variables is exploited by agents to alternate between tasks. In addition, we coordinate the actions of the multiple agents acting on their local states through these multipliers, which are gossiped through a communication network, eliminating the need to access other agent states. By these means, we propose a distributed multi-agent assignment protocol with theoretical feasibility guarantees that we corroborate in a monitoring numerical experiment.