Title :
An investigation of guarding a territory problem in a grid world
Author :
Xiaosong Lu ; Schwartz, H.M.
Author_Institution :
Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, ON, Canada
fDate :
June 30 2010-July 2 2010
Abstract :
A game of guarding a territory in a grid world is proposed in this paper. A defender tries to intercept an invader before he reaches the territory. Two reinforcement learning algorithms are applied to make two players learn their optimal policies simultaneously. Minimax-Q learning algorithm and Win-or-Learn-Fast Policy Hill-Climbing learning algorithm are introduced and compared. Simulation results of two reinforcement learning algorithms are analyzed.
Keywords :
game theory; grid computing; learning (artificial intelligence); minimax techniques; Hill-Climbing learning algorithm; grid world; minimax-Q learning algorithm; optimal policy; reinforcement learning algorithm; win-or-learn-fast policy; Algorithm design and analysis; Analytical models; Differential equations; Fuzzy reasoning; Learning systems; Mobile robots; Multiagent systems; Security; Stochastic processes; Surveillance;
Conference_Titel :
American Control Conference (ACC), 2010
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-7426-4
DOI :
10.1109/ACC.2010.5530771
