April 10, 2024, 4:42 a.m. | Yixuan Zhang, Dongyan Huo, Yudong Chen, Qiaomin Xie

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.06023v1 Announce Type: cross
Abstract: Motivated by Q-learning, we study nonsmooth contractive stochastic approximation (SA) with constant stepsize. We focus on two important classes of dynamics: 1) nonsmooth contractive SA with additive noise, and 2) synchronous and asynchronous Q-learning, which features both additive and multiplicative noise. For both dynamics, we establish weak convergence of the iterates to a stationary limit distribution in Wasserstein distance. Furthermore, we propose a prelimit coupling technique for establishing steady-state convergence and characterize the limit of …

abstract approximation arxiv asynchronous convergence cs.lg dynamics features focus math.oc math.pr noise q-learning state stat.ml stochastic study type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Software Engineer, Machine Learning (Tel Aviv)

@ Meta | Tel Aviv, Israel

Senior Data Scientist- Digital Government

@ Oracle | CASABLANCA, Morocco