Surveillance Evasion Through Bayesian Reinforcement Learning

• URL: http://arxiv.org/abs/2109.14811v1
• Date: Thu, 30 Sep 2021 02:29:21 GMT
• Title: Surveillance Evasion Through Bayesian Reinforcement Learning
• Authors: Dongping Qi, David Bindel, Alexander Vladimirsky
• Abstract summary: We consider a 2D continuous path planning problem with a completely unknown intensity of random termination. Those Observers' surveillance intensity is a priori unknown and has to be learned through repetitive path planning.
• Score: 78.79938727251594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider a 2D continuous path planning problem with a completely unknown intensity of random termination: an Evader is trying to escape a domain while minimizing the cumulative risk of detection (termination) by adversarial Observers. Those Observers' surveillance intensity is a priori unknown and has to be learned through repetitive path planning. We propose a new algorithm that utilizes Gaussian process regression to model the unknown surveillance intensity and relies on a confidence bound technique to promote strategic exploration. We illustrate our method through several examples and confirm the convergence of averaged regret experimentally.

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