@conference {1500420,
	title = {Policy Learning for Continuous Space Security Games using Neural Networks },
	booktitle = {AAAI conference on Artificial Intelligence (AAAI-18)},
	year = {2018},
	abstract = {A wealth of algorithms centered around (integer) linear programming have been proposed to compute equilibrium strategies in security games with discrete states and actions. However, in practice many domains possess continuous state and
action spaces. In this paper, we consider a continuous space
security game model with infinite-size action sets for players and present a novel deep learning based approach to extend the existing toolkit for solving security games. Specifically, we present (i) OptGradFP, a novel and general algorithm that searches for the optimal defender strategy in a parameterized continuous search space, and can also be used
to learn policies over multiple game states simultaneously;
(ii) OptGradFP-NN, a convolutional neural network based
implementation of OptGradFP for continuous space security
games. We demonstrate the potential to predict good defender
strategies via experiments and analysis of OptGradFP and
OptGradFP-NN on discrete and continuous game settings.},
	author = {Kamra, Nitin and Gupta, Umang and Fang, Fei and Liu, Yan and Tambe, Milind}
}
