arXiv cs.AI· Duncan Eddy, Mahdi Al-Husseini, Mykel J. Kochenderfer·· 14 小时前AI 评分31
OrbitalGym：规划算法何时决定轨道博弈结果
Where Planning Changes the Outcome of Orbital Games
AI 导读
论文提出 OrbitalGym，一个基于 JAX 的仿真环境，用于研究规划算法与系统设计何时决定对抗性轨道遭遇的结果，可将博弈编译为批量化 GPU 代码进行大规模并行仿真。
正文 · 原文
Abstract: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.
| Comments: | IEEE Aerospace Conference 2027 |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.37686 [cs.MA] |
| (or arXiv:2610.37686v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37686 arXiv-issued DOI via DataCite |
Submission history
From: Duncan Eddy [view email]
[v1]
Fri, 16 Oct 2026 22:53:33 UTC (2,532 KB)
来源：arXiv cs.AI · arxiv.org