We propose the adaptive memory sampling method which aims to find the distribution of the sampling length by using posterior sampling to update it iteratively.
The adaptive memory sampling method is proposed which aims to find the distribution of the sampling length by using posterior sampling to update it ...
Abstract—Poker game has become one of the most prevailing benchmark environment to discover algorithms for sequential games with imperfect information ...
Solving Poker Games Efficiently: Adaptive Memory based Deep Counterfactual Regret Minimization ; Shuqing Shi ; Xiaobin Wang ; Dong Hao ; Zhiyou Yang.
Solving Poker Games Efficiently: Adaptive Memory based Deep Counterfactual Regret Minimization. https://doi.org/10.1109/ijcnn55064.2022.9892417.
Solving Poker Games Efficiently: Adaptive Memory based Deep Counterfactual Regret Minimization. Shuqing Shi Xiaobin Wang Dong Hao Zhiyou Yang Hong Qu.
This paper introduces Deep Counter- factual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to ...
Solving Poker Games Efficiently: Adaptive Memory based Deep Counterfactual Regret Minimization · pdf icon · hmtl icon · Shuqing Shi, Xiaobin Wang, Dong Hao ...
A new sampling variant of Counterfactual Regret Minimization, called Targeted CFR, is described, which outperforms other sampling variants on certain types ...
Nov 1, 2018 · This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks.
Missing: Efficiently: Adaptive Memory based