8 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.AI2021★ 1 cited
D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network
Huale Li, Xuan Wang, Zengyue Guo +2
Counterfactual Regret Minimization (CFR)} is the popular method for finding approximate Nash equilibrium in two-player zero-sum games with imperfect information. CFR solves games b…
cs.LG2020
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Huale Li, Xuan Wang, Fengwei Jia +4
Counterfactual regret minimization (CFR) is a popular method to deal with decision-making problems of two-player zero-sum games with imperfect information. Unlike existing studies…
cs.GT2020★ 8 cited
Solving imperfect-information games via exponential counterfactual regret minimization
Huale Li, Xuan Wang, Shuhan Qi +4
In general, two-agent decision-making problems can be modeled as a two-player game, and a typical solution is to find a Nash equilibrium in such game. Counterfactual regret minimiz…