Multi-agent assignment via state augmented reinforcement learning
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 3 Jun 2024.
Builds on
11 of 23 references.
References · 11
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
- 2018Reward Constrained Policy Optimizationcited by 397
- 2019Constrained Reinforcement Learning Has Zero Duality Gapcited by 144
- 2017Uncertainty-Aware Reinforcement Learning for Collision Avoidancecited by 86
- 2019Safe Policies for Reinforcement Learning via Primal-Dual Methodscited by 75
- 2019Reinforcement Learning with Convex Constraintscited by 57
- 2021State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewardscited by 37
- 2018A Distributed Version of the Hungarian Method for Multi-Robot Assignmentcited by 7
- 2020Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfigurationcited by 7
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Citing papers per year: 2026: 3.
3 papers.