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Where Planning Changes the Outcome of Orbital Games — arXiv:2610.37686

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.37686
License (as archived): CC0-1.0
Archived: 2026-10-31T06:03:03.472Z
SHA-256 of the archived text: fd6cb22cded8869b7672d7a607e02529f42a2544b4751e75d716ccd1e2d1703e
Stable URL of this page: https://metraipolis.com/documento/dc_4e710db38819

How to cite

Where Planning Changes the Outcome of Orbital Games — arXiv:2610.37686 — arXiv (en), https://arxiv.org/abs/2610.37686 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4e710db38819 (Metraipolis archive dc_4e710db38819, sha256 fd6cb22cded8869b7672d7a607e02529f42a2544b4751e75d716ccd1e2d1703e)

BibTeX

@misc{dc4e710db38819,
  title = {Where Planning Changes the Outcome of Orbital Games — arXiv:2610.37686},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.37686},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4e710db38819; sha256 fd6cb22cded8869b7672d7a607e02529f42a2544b4751e75d716ccd1e2d1703e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.37686. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper introduces OrbitalGym, a simulation environment for studying when planning algorithms or system design determine the outcome of adversarial orbital encounters. In an orbital game, two non-cooperative teams of satellites maneuver during rendezvous and proximity operations to win a differential-reward objective. Advances in terrestrial game playing have prompted many methods for orbital games, but studies typically evaluate one method in one scenario, often against a passive or random defender. It is unclear whether better planning matters once vehicle capability (maneuverability, sensing, communications) and adversarial responses are considered. OrbitalGym is a JAX-based environment supporting varying numbers of agents, state representations (position, velocity, attitude, fuel), explicit or implicit communication, and proximity or conical field-of-view sensors. It compiles games to batched GPU code, enabling large-scale parallel simulation of policies across many configurations. This identifies regimes where policy choice affects outcomes and regimes where system capability dominates. We study two close-range games: pursuit-evasion, where one spacecraft tries to capture another, and lady-bandit-guard, where a guard prevents an attacker from reaching a protected asset. We compare PD, LQR, model predictive path integral control, and Monte Carlo tree search over ratios of thrust and propellant, initial separation, game duration, sensor capability, and the definition of capture. In pursuit-evasion the propellant ratio decides most encounters: against five of six evader policies, pursuer policy changes capture probability by 10 percent or more in at most 15 of 90 capability configurations. In lady-bandit-guard, policy matters more: against bandits able to reach the asset, a better guard policy raises defense probability by at least 10 percent in 56 to 74 of 90 configurations.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA) ; Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.37686 — 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 2026-10-31T06:03:01.112Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).