基于距離信息的追逃策略: 信念狀態連續隨機博弈

陳靈敏; 馮宇; 李永強

doi:10.16383/j.aas.c230018

基于距離信息的追逃策略: 信念狀態連續隨機博弈

doi: 10.16383/j.aas.c230018

1.
浙江工業大學信息工程學院杭州 313000

基金項目: 國家自然科學基金(61973276, 62073294), 浙江省自然科學基金(LZ21F030003)資助

詳細信息

作者簡介:
陳靈敏：浙江工業大學信息工程學院碩士研究生. 2020年獲得紹興文理學院學士學位. 主要研究方向為博弈論與機器學習在決策問題中的應用. E-mail: 2112003096@zjut.edu.cn

馮宇：浙江工業大學信息工程學院教授. 2011 年獲得法國南特礦業大學博士學位. 主要研究方向為網絡化控制系統, 分布式濾波, 不確定系統的魯棒分析與控制, 以及博弈論與機器學習在決策問題中的應用. 本文通信作者. E-mail: yfeng@zjut.edu.cn

李永強：浙江工業大學信息工程學院副教授. 2014 年獲得北京交通大學博士學位. 主要研究方向為強化學習, 非線性控制以及深度學習. E-mail: yqli@zjut.edu.cn

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- 被引次數: 0
出版歷程
- 收稿日期: 2023-01-12
- 錄用日期: 2023-04-04
- 網絡出版日期: 2023-05-11
- 刊出日期: 2024-04-26

Distance Information Based Pursuit-evasion Strategy: Continuous Stochastic Game With Belief State

1.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 313000

Funds: Supported by National Natural Science Foundation of China (61973276, 62073294) and Natural Science Foundation of Zhejiang Province (LZ21F030003)

More Information

Author Bio:
CHEN Ling-Min　Master student at College of Information Engineering, Zhejiang University of Technology. She received her bachelor degree from Shaoxing University in 2020. Her research interest covers game theory and machine learning in decision-making

FENG Yu　Professor at College of Information Engineering, Zhejiang University of Technology. He received his Ph.D. degree from Ecole des Mines de Nantes in 2011. His research interest covers networked control systems, distributed filtering, and robust analysis and control for uncertainty systems, and applications of game theory and machine learning in decision-making. Corresponding author of this paper

LI Yong-Qiang　Associate professor at College of Information Engineering, Zhejiang University of Technology. He received his Ph.D. degree from Beijing Jiaotong University in 2014. His research interest covers reinforcement learning, nonlinear control and deep learning

摘要

摘要: 追逃問題的研究在對抗、追蹤以及搜查等領域極具現實意義. 借助連續隨機博弈與馬爾科夫決策過程(Markov decision process, MDP), 研究使用測量距離求解多對一追逃問題的最優策略. 在此追逃問題中, 追捕群體僅領導者可測量與逃逸者間的相對距離, 而逃逸者具有全局視野. 追逃策略求解被分為追博弈與馬爾科夫決策兩個過程. 在求解追捕策略時, 通過分割環境引入信念區域狀態以估計逃逸者位置, 同時使用測量距離對信念區域狀態進行修正, 構建起基于信念區域狀態的連續隨機追博弈, 并借助不動點定理證明了博弈平穩納什均衡策略的存在性. 在求解逃逸策略時, 逃逸者根據全局信息建立混合狀態下的馬爾科夫決策過程及相應的最優貝爾曼方程. 同時給出了基于強化學習的平穩追逃策略求解算法, 并通過案例驗證了該算法的有效性.
- 追逃問題 /
- 信念區域狀態 /
- 連續隨機博弈 /
- 馬爾科夫決策過程 /
- 強化學習
Abstract: The pursuit-evasion problem is of great importance in the fields of confrontation, tracking and searching. In this paper, we are focused on the study of optimal strategies for solving the multi-pursuits and single-evader problem with only measured distances within the framework of continuous stochastic game and Markov decision process (MDP). In such problem, only the leader of pursuits can measure its relative distance with respect to the evader, while the evader has a global view. The strategies of the pursuits and evader are established via two steps: The pursuit game and the MDP. For the pursuits＇ strategy, the belief region state is introduced by partitioning the environment to estimate the evader＇s position, and the belief region state is further corrected by using the measured distances. A continuous stochastic pursuit game is then formed based on the belief region state, and the existence of stationary Nash equilibrium strategies is shown through the fixed-point theorem. For the evader＇s strategy, an MDP with the global states is established and the underlying optimal Bellman equation is devised. Moreover, a reinforcement learning based algorithm is presented for stationary pursuit-evasion strategies computation, and an example is also included to exhibit the effectiveness of the current method.
- Pursuit-evasion problem /
- belief region state /
- continuous stochastic game /
- Markov decision process (MDP) /
- reinforcement learning

HTML全文

圖 1 追逃問題環境

Fig. 1 Environment of pursuit-evasion problem

下載: 全尺寸圖片幻燈片

圖 2 (a) $ L $個區域; (b) 追捕群體的劃分

Fig. 2 (a) $ L $ regions; (b) Division of pursuit group

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圖 3 警戒區域

Fig. 3 Warning area

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圖 4 第$ m $個區域

Fig. 4 The $m\text{-}{\rm{th}}$ area

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圖 5 預測距離

Fig. 5 Prediction distance

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圖 6 地圖尺寸

Fig. 6 Size of map

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圖 7 追博弈中追捕群體的收益

Fig. 7 Pursuits＇ reward in the pursuit game

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圖 8 MDP中逃逸者的收益

Fig. 8 Evader＇s reward in MDP

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圖 9 算法測試過程

Fig. 9 Algorithm testing process

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圖 10 追捕群體與逃逸者的運動軌跡圖

Fig. 10 Trajectories of pursuits and evader

下載: 全尺寸圖片幻燈片

表 1 結果對比

Table 1 Result comparison

算法	捕捉平均步數	捕捉成功率
本文算法	41	95%
本文算法(未修正)	43	87%
MAPPO^[40]	88	59%
MASAC^[41]	85	61%
MADDPG^[42]	99	56%
幾何估計追捕^[33]	78	72%
基于三角定位追捕^[34]	61	94%
至少一人全局視野追捕^[23]	62	85%
自動追蹤追捕^[36]	82	71%
自適應切換追捕^[37]	65	66%
隨機策略	152	10%

下載: 導出CSV

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