44 citations · 128 across the 16 of their papers we have counts for
12 papers · 1 filter
Efficient Sequential Calibration with Error Bound
Zihan Zhang
We study the online binary sequential calibration problem. A recent breakthrough by \citet{dagan2024breaking} overcomes the classical \(T^{2/3}\) barrier for calibration error. Bui…
Settling the Sample Complexity of Online Reinforcement Learning
Zihan Zhang, Yuxin Chen, Jason D. Lee +1
A central issue lying at the heart of online reinforcement learning (RL) is data efficiency. While a number of recent works achieved asymptotically minimal regret in online RL, the…
Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes
Zihan Zhang, Qiaomin Xie
We develop several provably efficient model-free reinforcement learning (RL) algorithms for infinite-horizon average-reward Markov Decision Processes (MDPs). We consider both onlin…
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning
Zihan Zhang, Yuhang Jiang, Yuan Zhou +1
In this paper, we study the episodic reinforcement learning (RL) problem modeled by finite-horizon Markov Decision Processes (MDPs) with constraint on the number of batches. The mu…
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies
Zihan Zhang, Xiangyang Ji, Simon S. Du
This paper gives the first polynomial-time algorithm for tabular Markov Decision Processes (MDP) that enjoys a regret bound \emph{independent on the planning horizon}. Specifically…
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits
Zihan Zhang, Xiangyang Ji, Yuan Zhou
We study the optimal batch-regret tradeoff for batch linear contextual bandits. For any batch number , number of actions , time horizon , and dimension , we provide an…
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Zihan Zhang, Jiaqi Yang, Xiangyang Ji +1
This paper presents new \emph{variance-aware} confidence sets for linear bandits and linear mixture Markov Decision Processes (MDPs). With the new confidence sets, we obtain the fo…
Nearly Minimax Optimal Reward-free Reinforcement Learning
Zihan Zhang, Simon S. Du, Xiangyang Ji
We study the reward-free reinforcement learning framework, which is particularly suitable for batch reinforcement learning and scenarios where one needs policies for multiple rewar…
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Zihan Zhang, Xiangyang Ji, Simon S. Du
Episodic reinforcement learning and contextual bandits are two widely studied sequential decision-making problems. Episodic reinforcement learning generalizes contextual bandits an…
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
Zihan Zhang, Yuan Zhou, Xiangyang Ji
In this paper we consider the problem of learning an -optimal policy for a discounted Markov Decision Process (MDP). Given an MDP with states, actions, the discount fact…
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition
Zihan Zhang, Yuan Zhou, Xiangyang Ji
We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with states, actions, and episode length . We prop…
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function
Zihan Zhang, Xiangyang Ji
We present an algorithm based on the \emph{Optimism in the Face of Uncertainty} (OFU) principle which is able to learn Reinforcement Learning (RL) modeled by Markov decision proces…