Optimal Cislunar Architecture Design Using Monte Carlo Tree Search Methods

被引：4

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.

引用

页数：30

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