We explore space traffic management as an application of collision-free navigation in multi-agent systems where vehicles have limited observation and communication ranges. We investigate the effectiveness of transferring a collision avoidance multi-agent reinforcement (MARL) model trained on a ground environment to a space one. We demonstrate that the transfer learning model outperforms a model that is trained directly on the space environment. Furthermore, we find that our approach works well even when we consider the perturbations to satellite dynamics caused by the Earth's oblateness. Finally, we show how our methods can be used to evaluate the benefits of information-sharing between satellite operators in order to improve coordination.
翻译:我们探索将空间交通管理作为多试剂系统无碰撞导航的一种应用,在这些系统中,车辆的观测和通信范围有限;我们调查把在地面环境中培训的避免碰撞多剂强化模型(MARL)转让给空间模型的有效性;我们证明,转让学习模型优于直接就空间环境培训的模型;此外,我们发现,即使我们考虑到地球的模糊性对卫星动态造成的干扰,我们的方法仍然有效;最后,我们表明,如何利用我们的方法来评估卫星操作者之间分享信息的好处,以改进协调。