378 citations · 1.8k across the 48 of their papers we have counts for
4 papers · 1 filter
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Wenhao Zhan, Shicong Cen, Baihe Huang +3
Policy optimization, which finds the desired policy by maximizing value functions via optimization techniques, lies at the heart of reinforcement learning (RL). In addition to valu…
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Gen Li, Yuxin Chen, Yuejie Chi +2
Low-complexity models such as linear function representation play a pivotal role in enabling sample-efficient reinforcement learning (RL). The current paper pertains to a scenario…
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps
Gen Li, Changxiao Cai, H. Vincent Poor +1
Eigenvector perturbation analysis plays a vital role in various data science applications. A large body of prior works, however, focused on establishing eigenvector pert…
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
Gen Li, Yuting Wei, Yuejie Chi +1
The softmax policy gradient (PG) method, which performs gradient ascent under softmax policy parameterization, is arguably one of the de facto implementations of policy optimizatio…