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most citedNearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes

4 citations · 9 across the 17 of their papers we have counts for

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cs.LG2022★ 4 cited

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes

Jiafan He, Heyang Zhao, Dongruo Zhou +1

We study reinforcement learning (RL) with linear function approximation. For episodic time-inhomogeneous linear Markov decision processes (linear MDPs) whose transition probability…

cs.LG2022

Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits

Heyang Zhao, Dongruo Zhou, Jiafan He +1

We study the problem of online generalized linear regression in the stochastic setting, where the label is generated from a generalized linear model with possibly unbounded additiv…