Optimal Cislunar Architecture Design Using Monte Carlo Tree Search Methods

被引：3
|
作者：
Klonowski, Michael [1 ]
Holzinger, Marcus J. [1 ]
Fahrner, Naomi Owens [2 ]
机构：
[1] Univ Colorado, Smead Aerosp Engn Sci, 3775 Discovery Dr, Boulder, CO 80303 USA
[2] Ball Aerosp, 10 Longs Peak Dr, Broomfield, CO 80021 USA
来源：
JOURNAL OF THE ASTRONAUTICAL SCIENCES | 2023年 / 70卷 / 03期
关键词：
Monte Carlo Tree Search; Space domain awareness; Reinforcement learning; Cislunar architecture; MULTIOBJECTIVE OPTIMIZATION;
D O I：
10.1007/s40295-023-00383-x
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
A novel multi-objective Monte Carlo Tree Search (MO-MCTS) algorithm is developed and implemented for use in architecture design problems. This algorithm is used with two well-known problems with known solutions in order to verify its performance. It is then used in a highly nonlinear Cislunar architecture design problem with no known analytical solutions. The results of this implementation display the ability of MO-MCTS to effectively navigate the state space of mixed integer nonlinear programming problems and emphasize the versatility of MO-MCTS for designing critical Cislunar architecture.
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页数：30
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