Robust Multi-agent Patrolling Strategies Using Reinforcement Learning

Lauri, Fabrice; Koukam, Abderraf́ıâa

doi:10.1007/978-3-319-12970-9_17

Cited by 6 publications

(1 citation statement)

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“…Other games and applications using RL have also been proposed as, for example, the patrolling of an area [7]. In this scenario, multiple agents learn an MDP using Q-Learning to patrol their environment.…”

Section: Reinforcement Learningmentioning

confidence: 99%

A Learning Invader for the “Guarding a Territory” Game

Raslan

Schwartz

Givigi

2016

J Intell Robot Syst

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This paper explores the use of a learning algorithm in the "guarding a territory" game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is going to learn. Furthermore, in our case the guard is superior (faster) to the invader. We will also consider using models with non-holonomic constraints. A control system is designed and optimized for the invader to play the game and reach Nash Equilibrium. The paper shows how the learning system is able to adapt itself. The system's performance is evaluated through different simulations and compared to the Nash Equilibrium. Experiments with real robots were conducted and verified our simulations in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.

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Section: Reinforcement Learningmentioning

confidence: 99%