Multi-agent assignment via state augmented reinforcement learning

Juan Andres Bazerque; Leopoldo Agorio; Miguel Calvo-Fullana; Santiago Paternain; Sean Van Alen

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Multi-agent assignment via state augmented reinforcement learning

Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana, Santiago Paternain, Juan Andres Bazerque

Systems and Control · Submitted 3 Jun 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim By these means, we propose a distributed multi-agent assignment protocol with theoretical feasibility guarantees that we corroborate in a monitoring numerical experiment.

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arXiv ID2406.01782 (version 1)

Pith IDTIU6SHEK, Pith's permanent record of this paper and its review

CitationJuan Andres Bazerque, Leopoldo Agorio, Miguel Calvo-Fullana, Santiago Paternain, Sean Van Alen. "Multi-agent assignment via state augmented reinforcement learning." arXiv:2406.01782 (2024). https://pith.science/paper/TIU6SHEK

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,608 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.31 · human participants 0.02 · controlled comparison 0.16 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.53 · mentions code or data availability 0.02.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	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.	gap 0.54	0.40	0.14	asserted 1.00
s1	Instead, we recur to a state augmentation approach in which the oscillation of dual variables is exploited by agents to alternate between tasks.	method 0.95	0.83	0.14	asserted 0.99
s2	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.	method 0.98	0.85	0.11	asserted 1.00
s3	By these means, we propose a distributed multi-agent assignment protocol with theoretical feasibility guarantees that we corroborate in a monitoring numerical experiment.	contribution 0.88	0.94	0.70	asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.92.

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arXiv listing abstract v1

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full text
figures and tables
references and citing sentences
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P abstract

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S sections
T body sentences
C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

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.

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2 arXiv papers cite this in 2 sentences

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What it cites · 2 sentences without a confident label

Cites as background 1 paper

Reinforcement Learning (RL) has experienced growing interest as a methodology for designing optimal controllers in cases where system dynamics are intractable or unknown [ref] .

Uncertainty-Aware Reinforcement Learning for Collision Avoidance · Introduction · 0.93

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