标签筛选：马尔可夫决策过程中的策略优化 - 论文列表

Dynamic Discounted Counterfactual Regret Minimization (CFR) is a family of iterative algorithms showing promising results in solving imperfect-information games. Recent novel CFR variants (e.g., CFR+, DCFR) have significantly improved the convergence rate of the vanilla CFR. The key to these CFR variants’ performance is weighting each iteration non-uniformly, i.e., discounting earlier iterations. However, these algorithms use a fixed, manually-specified scheme to weight each iteration, which eno

动态折扣反事实遗憾最小化马尔可夫决策过程中的策略优化不完全信息博弈求解迭代算法收敛性提升动态权重学习

本文提出了动态折扣反事实遗憾最小化(DDCFR)，这是首个通过动态学习方法对迭代进行折扣的框架。通过将CFR迭代过程形式化为马尔可夫决策过程，DDCFR实现了更快的收敛速度和更高的性能，具备强大的泛化能力，显著提升了解决不完全信息博弈的效率和潜力。