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pith:AZNIU77B

pith:2026:AZNIU77BOJ4W2ZROBHQD3MQZIL
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Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems

Fangfei Li, Jing Liu, Luca Ballotta, Ruggero Carli, Yang Tang, Yangyang Yang

The Partition Multilinear Extension supplies unbiased marginal gradients from submodular difference rewards for decentralized categorical policies.

arxiv:2605.13269 v1 · 2026-05-13 · eess.SY · cs.SY

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Claims

C1strongest claim

We prove a stagewise 1/2-approximation guarantee and sublinear dynamic regret in slowly varying environments, measured by the path length of the optimal PME marginals.

C2weakest assumption

The team utility function is submodular, which is required for submodular difference rewards to supply unbiased PME marginal-gradient information.

C3one line summary

SubMAPG uses a new Partition Multilinear Extension to derive unbiased policy gradients from submodular difference rewards, delivering 1/2-approximation and sublinear dynamic regret for online distributed task allocation in open multi-agent systems.

References

138 extracted · 138 resolved · 2 Pith anchors

[1] IEEE Transactions on Automatic Control , year=
[2] Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks , year=
[3] Minimax Persistent Monitoring of a network system , author=. Automatica , volume=. 2023 , publisher= 2023
[4] A sub-modular receding horizon solution for mobile multi-agent persistent monitoring , author=. Automatica , volume=. 2021 , publisher= 2021
[5] IEEE Transactions on Robotics , volume= 2022
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First computed 2026-05-18T02:44:49.291442Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

065a8a7fe172796d662e09e03db21942f08a82e958204274b2a0c48e44e6f2f6

Aliases

arxiv: 2605.13269 · arxiv_version: 2605.13269v1 · doi: 10.48550/arxiv.2605.13269 · pith_short_12: AZNIU77BOJ4W · pith_short_16: AZNIU77BOJ4W2ZRO · pith_short_8: AZNIU77B
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/AZNIU77BOJ4W2ZROBHQD3MQZIL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 065a8a7fe172796d662e09e03db21942f08a82e958204274b2a0c48e44e6f2f6
Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-05-13T09:48:44Z",
    "title_canon_sha256": "947aecb07d926e6048f5fd802c2c57f334eef73060c6b60109e77630a16d670d"
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