2 papers
cs.LG2025
Adaptive Resolving Methods for Markov Decision Processes with Function Approximations
Jiashuo Jiang, Yinyu Ye, Yiming Zong
Learning the optimal policy for Markov decision process problems (MDPs) from samples is a fundamental problem in online and data-driven decision-making. Function approximations are…
cs.LG2024
Achieving Instance-dependent Sample Complexity for Constrained Markov Decision Process
Jiashuo Jiang, Yinyu Ye
We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequen…