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Dual-Agent Reinforcement Learning for Vehicle Scheduling and Multi-Energy Dispatch in Electric-Hydrogen-Transport Systems — arXiv:2610.40116

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40116
License (as archived): CC0-1.0
Archived: 2026-11-01T03:00:47.197Z
SHA-256 of the archived text: 60800e76406ff8ecf1ac6af313ff7b00bd69f59501aa104e818747378ea2aaa4
Stable URL of this page: https://metraipolis.com/documento/dc_586a7e61d72e

How to cite

Dual-Agent Reinforcement Learning for Vehicle Scheduling and Multi-Energy Dispatch in Electric-Hydrogen-Transport Systems — arXiv:2610.40116 — arXiv (en), https://arxiv.org/abs/2610.40116 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_586a7e61d72e (Metraipolis archive dc_586a7e61d72e, sha256 60800e76406ff8ecf1ac6af313ff7b00bd69f59501aa104e818747378ea2aaa4)

BibTeX

@misc{dc586a7e61d72e,
  title = {Dual-Agent Reinforcement Learning for Vehicle Scheduling and Multi-Energy Dispatch in Electric-Hydrogen-Transport Systems — arXiv:2610.40116},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40116},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_586a7e61d72e; sha256 60800e76406ff8ecf1ac6af313ff7b00bd69f59501aa104e818747378ea2aaa4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40116. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Electric-hydrogen-transport systems (EHTSs) require coordinated scheduling of electric and hydrogen vehicles together with multi-energy dispatch. These operations form a sequential decision process: upstream vehicle scheduling (VS) determines service allocation and order, thereby endogenously shaping the electricity and hydrogen demands faced by downstream energy dispatch (ED). This paper develops a dual-agent reinforcement learning framework tailored to the heterogeneous decision structures. The vehicle-scheduling agent combines proximal policy optimisation (PPO) with a Plackett-Luce policy to learn vehicle-priority permutations, which are then mapped to feasible charger and refueller assignments through a greedy allocation. The energy-dispatch agent employs twin delayed deep deterministic policy gradient (TD3) for continuous storage and energy-flow control. The directional coupling between the two decision layers is analysed, revealing that updates to the upstream scheduling policy can shift the demand distribution experienced by the downstream dispatcher. A three-stage training mechanism is therefore introduced, combining layer-wise pretraining with concurrent fine-tuning to mitigate the effects of scheduling-induced distribution shifts on downstream ED learning. Year-long case studies show that the proposed framework achieves the lowest vehicle-service delay and energy cost among the 11 evaluated methods, demonstrating effective coordination between vehicle scheduling and multi-energy dispatch.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv eess.SY, nuovi lavori del giorno).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.40116 — 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 2026-11-01T02:25:30.756Z dalla pagina https://arxiv.org/list/eess.SY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).