Using Cognitive Behavioral Learning in Multi-agent Pursuit-Evasion Game

2014 ◽  
Author(s):  
Jong Yih Kuo ◽  
Chien-Hung Liu ◽  
Fang-Wen Lee
Keyword(s):  
Cognitive Behavioral ◽  
Behavioral Learning ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Multi Agent

scholarly journals A Stochastic Characterization of the Capture Zone in Pursuit-Evasion Games

Games ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 54
Author(s):  
Simone Battistini
Keyword(s):  
Optimal Strategies ◽  
Capture Zone ◽  
Pursuit Evasion ◽  
Guidance Strategies ◽  
Stochastic Characterization ◽  
Evasion Game ◽  
Multi Agent ◽  
Planning Problems ◽  
Insight Into

Pursuit-evasion games are used to define guidance strategies for multi-agent planning problems. Although optimal strategies exist for deterministic scenarios, in the case when information about the opponent players is imperfect, it is important to evaluate the effect of uncertainties on the estimated variables. This paper proposes a method to characterize the game space of a pursuit-evasion game under a stochastic perspective. The Mahalanobis distance is used as a metric to determine the levels of confidence in the estimation of the Zero Effort Miss across the capture zone. This information can be used to gain an insight into the guidance strategy. A simulation is carried out to provide numerical results.

scholarly journals An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1433
Author(s):  
Kaifang Wan ◽  
Dingwei Wu ◽  
Yiwei Zhai ◽  
Bo Li ◽  
Xiaoguang Gao ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Superior Performance ◽  
State Variables ◽  
Multi Agent Systems ◽  
Adversarial Learning ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Multi Agent ◽  
Adversarial Attack

A pursuit–evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain. An online decision technique based on deep reinforcement learning (DRL) was developed in this paper to address the problem of environment sensing and decision-making in pursuit–evasion games. A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the limitation of the tedious state variables required for the traditionally complicated modeling process. To address the effects of errors between a model and a real scenario, this paper introduces adversarial disturbances. It also proposes a novel adversarial attack trick and adversarial learning MADDPG (A2-MADDPG) algorithm. By introducing an adversarial attack trick for the agents themselves, uncertainties of the real world are modeled, thereby optimizing robust training. During the training process, adversarial learning was incorporated into our algorithm to preprocess the actions of multiple agents, which enabled them to properly respond to uncertain dynamic changes in MASs. Experimental results verified that the proposed approach provides superior performance and effectiveness for pursuers and evaders, and both can learn the corresponding confrontational strategy during training.

scholarly journals Predictive pursuit-evasion game control method for approaching space non-cooperative target

2020 ◽  
Vol 53 (2) ◽  
pp. 14882-14887
Author(s):  
Yuan Chai ◽  
Jianjun Luo ◽  
Mingming Wang ◽  
Min Yu
Keyword(s):  
Control Method ◽  
Pursuit Evasion ◽  
Evasion Game

scholarly journals Collaborative Pursuit-Evasion Strategy of UAV/UGV Heterogeneous System in Complex Three-Dimensional Polygonal Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiao Liang ◽  
Honglun Wang ◽  
Haitao Luo
Keyword(s):  
Visual Field ◽  
Heterogeneous System ◽  
Three Dimensional ◽  
Unmanned Ground Vehicle ◽  
Worst Case ◽  
Ground Vehicle ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Evasion Strategy ◽  
Aerial Vehicle

The UAV/UGV heterogeneous system combines the air superiority of UAV (unmanned aerial vehicle) and the ground superiority of UGV (unmanned ground vehicle). The system can complete a series of complex tasks and one of them is pursuit-evasion decision, so a collaborative strategy of UAV/UGV heterogeneous system is proposed to derive a pursuit-evasion game in complex three-dimensional (3D) polygonal environment, which is large enough but with boundary. Firstly, the system and task hypothesis are introduced. Then, an improved boundary value problem (BVP) is used to unify the terrain data of decision and path planning. Under the condition that the evader knows the position of collaborative pursuers at any time but pursuers just have a line-of-sight view, a worst case is analyzed and the strategy between the evader and pursuers is studied. According to the state of evader, the strategy of collaborative pursuers is discussed in three situations: evader is in the visual field of pursuers, evader just disappears from the visual field of pursuers, and the position of evader is completely unknown to pursuers. The simulation results show that the strategy does not guarantee that the pursuers will win the game in complex 3D polygonal environment, but it is optimal in the worst case.

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