Maximal Coverage Problems with Routing Constraints using Cross-Entropy Monte Carlo Tree Search

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姓名

林寶德(Pao-TE Lin) 查詢紙本館藏

畢業系所

數學系

論文名稱

(Maximal Coverage Problems with Routing Constraints using Cross-Entropy Monte Carlo Tree Search)

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★ Localizing Complex Terrain for Quadruped Robots using Adaptive Submodularity	★ 使用成本效益生成樹的資訊軌跡規劃
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至系統瀏覽論文 (2024-8-1以後開放)

摘要(中)

最大覆蓋問題在路徑限制下是NP-hard問題。廣義成本收益演算法(GCB)能解決這樣的問題，並達到1/2(1-1/e)的近似最佳值。但是GCB和近似最佳解仍有有一段差距。此研究提出基於交叉熵的蒙地卡羅搜尋樹演算法 (CE-MCTS) 來解決這個問題。這個演算法包含三個部分: 演化演算法 (EA) 用於採樣分支、信賴上界策略 (UCB) 用於選擇展開和估計分布演算法 (EDA) 用於模擬。實驗證實了CE-MCTS在不同地圖及成本限制底下的效能比其他兩個演算法(GCB、EAMC)更好。

摘要(英)

The maximal coverage problems with routing constraints are NP-hard problems.
The generalized cost-benefit algorithm (GCB) is able to solve this problem with a $frac{1}{2}(1-frac{1}{e})widetilde{OPT}$ guarantee.
There is a space between the approximation optimal solution $(widetilde{OPT})$ and GCB performance.
In this research, the cross-entropy based Monte Carlo Tree Search algorithm (CE-MCTS) is proposed to solve this problem.
It consists of three parts: the evolutionary algorithm (EA) for sampling the branches, the upper confidence bound (UCB) policy for selections, and the estimation of distribution algorithm (EDA) for simulations.
The experiments demonstrate that the CE-MCTS outperforms benchmark approaches (e.g., GCB, EAMC) in different maps with various budget constraints.

關鍵字(中)

★ 次模性
★ 最大覆蓋問題
★ 交叉熵
★ 蒙地卡羅搜尋樹演算法

關鍵字(英)

★ Submodularity
★ Maximal coverage problem
★ Cross-Entropy
★ Monte-Carlo Tree Search

論文目次

摘要 ... i
Abstract.... ii
Acknowledgements.... iii
Contents .... iv
Figures.... vi
Tables .... xi
1 Introduction.... 1
1.1 Publication Note . . . . 3
2 Related work .... 4
2.1 Map exploration . . . . 4
2.2 Submodular maximization problems . . . . 5
2.3 Evolutionary algorithms . . . . 6
2.4 Cross-entropy method . . . . 6
2.5 Monte-carlo tree search . . . . 7
3 Background knowledge .... 8
3.1 Submodularity . . . . 8
3.2 CEM . . . . . 9
3.3 MCTS . . . . 11
4 Problem formulation.... 14
4.1 CE-MCTS . . . . 14
4.2 Denitions, theorems, and lemmas . . . . 17
4.3 Terminal conditions of CE-MCTS . . . . 20
5 Proposed algorithms.... 23
6 Experiments.... 26
6.1 Experiment setup . . . . 26
6.2 EX1: 2D coverage maximization . . . . 30
6.3 EX2: search problem . . . . 32
6.4 EX3: the feasibility of terminal criterion . . . . 32
7 Conclusions and future work .... 45
References .... 46

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指導教授

曾國師(Kuo-Shih Tseng)

審核日期

2022-7-25

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