9 citations · 47 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium
Chris Junchi Li, Dongruo Zhou, Quanquan Gu +1
We consider learning Nash equilibria in two-player zero-sum Markov Games with nonlinear function approximation, where the action-value function is approximated by a function in a R…
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
Dongruo Zhou, Quanquan Gu
Recent studies have shown that episodic reinforcement learning (RL) is not more difficult than contextual bandits, even with a long planning horizon and unknown state transitions.…
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
Jiafan He, Dongruo Zhou, Tong Zhang +1
We study the linear contextual bandit problem in the presence of adversarial corruption, where the reward at each round is corrupted by an adversary, and the corruption level (i.e.…
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…
Learning Neural Contextual Bandits Through Perturbed Rewards
Yiling Jia, Weitong Zhang, Dongruo Zhou +2
Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because…