Wenhao Zhan | DeepAI

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∙ 05/29/2023

How to Query Human Feedback Efficiently in RL?

Reinforcement Learning with Human Feedback (RLHF) is a paradigm in which...

0 Wenhao Zhan, et al. ∙

∙ 05/24/2023

Provable Offline Reinforcement Learning with Human Feedback

In this paper, we investigate the problem of offline reinforcement learn...

0 Wenhao Zhan, et al. ∙

∙ 05/17/2023

Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

This paper studies tabular reinforcement learning (RL) in the hybrid set...

1 Gen Li, et al. ∙

∙ 07/12/2022

PAC Reinforcement Learning for Predictive State Representations

In this paper we study online Reinforcement Learning (RL) in partially o...

5 Wenhao Zhan, et al. ∙

∙ 06/03/2022

Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games

We study decentralized policy learning in Markov games where we control ...

14 Wenhao Zhan, et al. ∙

∙ 02/09/2022

Offline Reinforcement Learning with Realizability and Single-policy Concentrability

Sample-efficiency guarantees for offline reinforcement learning (RL) oft...

0 Wenhao Zhan, et al. ∙

∙ 05/24/2021

Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence

Policy optimization, which learns the policy of interest by maximizing t...

7 Wenhao Zhan, et al. ∙

∙ 10/26/2020

Strong Privacy and Utility Guarantee: Over-the-Air Statistical Estimation

We consider the privacy problem of statistical estimation from distribut...

0 Wenhao Zhan, et al. ∙

∙ 09/28/2020

Delay Optimal Cross-Layer Scheduling Over Markov Channels with Power Constraint

We consider a scenario where a power constrained transmitter delivers ra...

0 Wenhao Zhan, et al. ∙

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