@conference {1499806,
	title = {Deep Fictitious Play for Games with Continuous Action Spaces },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (Extended Abstract) (AAMAS-19)},
	year = {2019},
	abstract = {Fictitious play has been a classic algorithm to solve two-player adversarial games with discrete action spaces. In this work we develop an approximate extension of fictitious play to two-player games with high-dimensional continuous action spaces. We use generative neural networks to approximate players{\textquoteright} best responses while also learning a differentiable approximate model to the players{\textquoteright} rewards given their actions. Both these networks are trained jointly with gradient-based optimization to emulate fictitious play. We explore our approach in zero-sum games, non zero-sum games and security game domains.},
	author = {Kamra, Nitin and Gupta, Umang and Kai Wang and Fang, Fei and Liu, Yan and Tambe, Milind}
}
