Learning Deep Mean Field Games for Modeling Large Population Behavior
Learning Deep Mean Field Games for Modeling Large Population Behavior
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发表时间：
2017-11
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通讯作者：
Jiachen Yang;X. Ye;Rakshit S. Trivedi;Huan Xu;H. Zha
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作者：
Jiachen Yang;X. Ye;Rakshit S. Trivedi;Huan Xu;H. Zha
我们考虑的问题，代表集体行为的大人口和预测的人口分布在离散状态空间的演变。一个离散时间平均场博弈（MFG）的动机是作为一个可解释的模型，建立在博弈论的理解个人行动的总效应和预测人口分布的时间演变。我们实现了MFG和马尔可夫决策过程（MDP）的合成表明，一个特殊的MFG是可约化的MDP。这使我们能够扩大平均场博弈论的范围，并通过深度逆强化学习来推断大型现实世界系统的MFG模型。我们的方法从真实的数据中学习MFG的奖励函数和前向动态，并且我们报告了真实世界社交媒体人群的平均场博弈模型的第一次实证测试。
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning. Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.