3 papers
cs.LG2025
Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs
Yukuan Wei, Xudong Li, Lin F. Yang
Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDPs) under the generative model. H…
cs.LG2024
Confident Natural Policy Gradient for Local Planning in -realizable Constrained MDPs
Tian Tian, Lin F. Yang, Csaba Szepesvári
The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives while maximizing c…
cs.LG2023
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation
Jiayi Huang, Han Zhong, Liwei Wang +1
To tackle long planning horizon problems in reinforcement learning with general function approximation, we propose the first algorithm, termed as UCRL-WVTR, that achieves both \emp…