6 citations · 8 across the 17 of their papers we have counts for
4 papers · 2 filters
Q-Learning with Stable Infinite-Dimensional Linear Function Approximation
Shengbo Wang
Q-learning with linear function approximation can be unstable because an arbitrary approximation architecture need not preserve the Bellman contraction. We develop a stable infinit…
Central Limit Theorem for Two-Time-Scale Approximate Distributionally Robust RL
Shengbo Wang, Zexi Zhang
Designing model-free algorithms for distributionally robust reinforcement learning (DRRL) poses fundamental challenges. The robust Bellman operator is nonlinear in the transition k…
Q-Measure-Learning for Continuous State RL: Efficient Implementation and Convergence
Shengbo Wang
We study reinforcement learning in infinite-horizon discounted Markov decision processes with continuous state spaces, where data are generated online from a single trajectory unde…
Achieving Dependence for Average-Reward Q-Learning with a New Contraction Principle
Zijun Chen, Zaiwei Chen, Nian Si +1
We present the convergence rates of synchronous and asynchronous Q-learning for average-reward Markov decision processes, where the absence of contraction poses a fundamental chall…