This work explores on-board planning for the single spacecraft, multiple ground station Earth-observing satellite scheduling problem through artificial neural network function approximation of state–action value estimates generated by Monte Carlo tree search (MCTS). An extensive hyperparameter search is conducted for MCTS on the basis of performance, safety, and downlink opportunity utilization to determine the best hyperparameter combination for data generation. A hyperparameter search is also conducted on neural network architectures. The learned behavior of each network is explored, and each network architecture’s robustness to orbits and epochs outside of the training distributions is investigated. Furthermore, each algorithm is compared with a genetic algorithm, which serves to provide a baseline for optimality. MCTS is shown to compute near-optimal solutions in comparison to the genetic algorithm. The state–action value networks are shown to match or exceed the performance of MCTS in six orders of magnitude less execution time, showing promise for execution on board spacecraft.

这项工作通过人工神经网络函数逼近由蒙特卡罗树搜索 (MCTS) 生成的状态-动作值估计，探索了单个航天器、多个地面站地球观测卫星调度问题的机载规划。基于性能、安全性和下行链路机会利用对 MCTS 进行广泛的超参数搜索，以确定用于数据生成的最佳超参数组合。还对神经网络架构进行了超参数搜索。探索了每个网络的学习行为，并研究了每个网络架构对训练分布之外的轨道和历元的鲁棒性。此外，每个算法都与遗传算法进行比较，遗传算法用于为最优性提供基线。与遗传算法相比，MCTS 被证明可以计算接近最优的解决方案。状态-动作价值网络被证明与 MCTS 的性能相匹配或超过了六个数量级的执行时间，显示出在航天器上执行的前景。