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Entropy-Augmented Multi-Objective Policy Optimization in Multiagent Systems — arXiv:2608.12534

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.12534
License (as archived): CC0-1.0
Archived: 2026-10-19T04:16:38.451Z
SHA-256 of the archived text: 8f3f695086057f1c7284734d5903d48fcf5fc1a6fc711763b21cd17fca328482
Stable URL of this page: https://metraipolis.com/documento/dc_e30deb8e577b

How to cite

Entropy-Augmented Multi-Objective Policy Optimization in Multiagent Systems — arXiv:2608.12534 — arXiv (en), https://arxiv.org/abs/2608.12534 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e30deb8e577b (Metraipolis archive dc_e30deb8e577b, sha256 8f3f695086057f1c7284734d5903d48fcf5fc1a6fc711763b21cd17fca328482)

BibTeX

@misc{dce30deb8e577b,
  title = {Entropy-Augmented Multi-Objective Policy Optimization in Multiagent Systems — arXiv:2608.12534},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.12534},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e30deb8e577b; sha256 8f3f695086057f1c7284734d5903d48fcf5fc1a6fc711763b21cd17fca328482},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.12534. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autonomous agent teams deployed in settings such as marine and extraterrestrial outposts must coordinate actions to achieve optimal outcomes across multiple competing objectives. Multi-objective evolutionary algorithms such as NSGA-II optimize for diversity in the objective space, but neglect diversity in the behavior space, possibly leading to premature convergence and a collapse in behaviors that may differentiate policies in different external conditions. To address this, we introduce an entropy-augmented policy evaluation strategy that incorporates an entropy bonus into agent fitness scores, discouraging behavioral homogeneity across the evolving population. By augmenting policy evaluation with a behavior-space diversity signal while preserving the underlying Pareto optimization framework, our method is designed to encourage exploration of behaviorally distinct policies in multiagent domains. We evaluate our approach across rover-domain experiments with qualitatively distinct reward structures and observe hypervolume improvements of up to 48% relative to the NSGA-II baseline, suggesting that behavioral diversity is a promising and underexplored direction for improving multi-objective multiagent evolutionary optimization.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-14 in cs.MA).
Categorie arXiv: Multiagent Systems (cs.MA) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2608.12534 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T04:12:45.939Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-08-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).