@conference {1499535,
	title = {DeepFP for Finding Nash Equilibrium in Continuous Action Spaces },
	booktitle = {Conference on Decision and Game Theory for Security, 2019},
	year = {2019},
	abstract = {Finding Nash equilibrium in continuous action spaces is achallenging problem and has applications in domains such as protecting geographic areas from potential attackers. We present DeepFP, anapproximate extension of fictitious play in continuous action spaces.DeepFP represents players{\textquoteright} approximate best responses via generativeneural networks which are highly expressive implicit density approximators. It additionally uses a game-model network which approximatesthe players{\textquoteright} expected payoffs given their actions, and trains the networksend-to-end in a model-based learning regime. Further, DeepFP allows using domain-specific oracles if available and can hence exploit techniquessuch as mathematical programming to compute best responses for structured games. We demonstrate stable convergence to Nash equilibriumon several classic games and also apply DeepFP to a large forest security domain with a novel defender best response oracle. We show thatDeepFP learns strategies robust to adversarial\&nbsp;},
	author = {Kamra, Nitin and Gupta, Umang and Kai Wang and Fang, Fei and Liu, Yan and Tambe, Milind}
}
