3 papers
cs.LG2024
Structure Matters: Dynamic Policy Gradient
Sara Klein, Xiangyuan Zhang, Tamer Başar +2
In this work, we study -discounted infinite-horizon tabular Markov decision processes (MDPs) and introduce a framework called dynamic policy gradient (DynPG). The framework dire…
cs.LG2024
Almost sure convergence rates of stochastic gradient methods under gradient domination
Simon Weissmann, Sara Klein, Waïss Azizian +1
Stochastic gradient methods are among the most important algorithms in training machine learning problems. While classical assumptions such as strong convexity allow a simple analy…
math.OC2023
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
Sara Klein, Simon Weissmann, Leif Döring
Markov Decision Processes (MDPs) are a formal framework for modeling and solving sequential decision-making problems. In finite-time horizons such problems are relevant for instanc…