2018/05/07 by Chun Kai Ling, Fei Fang, Ling, Chun Kai +3
Computer Science · Mathematics · Physics and Astronomy · #Artificial Intelligence in Games #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Multiagent Systems (cs.MA) #Reinforcement Learning in Robotics #cs.GT #cs.LG #cs.MA #stat.ML
paper · pdf · doi:10.48550/arxiv.1805.02777
Fixed typos and updated experimental results
openalex publication_date 2018/05/07 · arxiv created 2018/06/27 · arxiv updated 2018/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents. This paper deals with the relatively under-explored but equally important "inverse" setting, where the parameters of the underlying game are not known to all agents, but must be learned through observations. We propose a differentiable, end-to-end learning framework for addressing this task. In particular, we consider a regularized version of the game, equivalent to a particular form of quantal response equilibrium, and develop 1) a primal-dual Newton method for finding such equilibrium points in both normal and extensive form games; and 2) a backpropagation method that lets us analytically compute gradients of all relevant game parameters through the solution itself. This ultimately lets us learn the game by training in an end-to-end fashion, effectively by integrating a "differentiable game solver" into the loop of larger deep network architectures. We demonstrate the effectiveness of the learning method in several settings including poker and security game tasks.