论文标题
异步随机近似和$ Q $ - 学习的有限时间分析
Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning
论文作者
论文摘要
我们考虑使用具有加权无限型承包操作员的一般异步随机近似（SA）方案，并证明其在单个轨迹上的有限时间收敛速率有限。此外，我们将结果专门用于异步$ q $ - 学习。最终的界限与同步$ q $ - 学习最鲜明的界限相匹配，并且比以前的异步$ q $ - 学习的界限有所改善。
We consider a general asynchronous Stochastic Approximation (SA) scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence rate on a single trajectory. Additionally, we specialize the result to asynchronous $Q$-learning. The resulting bound matches the sharpest available bound for synchronous $Q$-learning, and improves over previous known bounds for asynchronous $Q$-learning.
