979 citations · 1k across the 15 of their papers we have counts for
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Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang +12
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performi…
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Yibo Wang, Wenhao Yang, Wei Jiang +5
Projection-free online learning has drawn increasing interest due to its efficiency in solving high-dimensional problems with complicated constraints. However, most existing projec…
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
Liangyu Zhang, Yang Peng, Wenhao Yang +1
We propose a novel generalization of constrained Markov decision processes (CMDPs) that we call the \emph{semi-infinitely constrained Markov decision process} (SICMDP). Particularl…
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
Tadashi Kozuno, Wenhao Yang, Nino Vieillard +10
In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model. Particularly, we analyze mirror descent value iteration (M…
Federated Reinforcement Learning with Environment Heterogeneity
Hao Jin, Yang Peng, Wenhao Yang +2
We study a Federated Reinforcement Learning (FedRL) problem in which agents collaboratively learn a single policy without sharing the trajectories they collected during agent-e…
Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs
Wenhao Yang, Xiang Li, Guangzeng Xie +1
Regularized MDPs serve as a smooth version of original MDPs. However, biased optimal policy always exists for regularized MDPs. Instead of making the coefficientλof regularized ter…