37 citations · 100 across the 13 of their papers we have counts for
13 papers
Frozen Policy Iteration: Computationally Efficient RL under Linear Realizability for Deterministic Dynamics
Yijing Ke, Zihan Zhang, Ruosong Wang
We study computationally and statistically efficient reinforcement learning under the linear realizability assumption, where any policy's -function is linear in a given st…
Misspecified -Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
Ally Yalei Du, Lin F. Yang, Ruosong Wang
The recent work by Dong & Yang (2023) showed for misspecified sparse linear bandits, one can obtain an -optimal policy using a polynomial number of samples when th…
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
Junyan Liu, Yunfan Li, Ruosong Wang +1
Existing metrics for reinforcement learning (RL) such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the a…
Provably Efficient Reinforcement Learning via Surprise Bound
Hanlin Zhu, Ruosong Wang, Jason D. Lee
Value function approximation is important in modern reinforcement learning (RL) problems especially when the state space is (infinitely) large. Despite the importance and wide appl…
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes
Runlong Zhou, Ruosong Wang, Simon S. Du
We study regret minimization for reinforcement learning (RL) in Latent Markov Decision Processes (LMDPs) with context in hindsight. We design a novel model-based algorithmic framew…
Variance-Aware Sparse Linear Bandits
Yan Dai, Ruosong Wang, Simon S. Du
It is well-known that for sparse linear bandits, when ignoring the dependency on sparsity which is much smaller than the ambient dimension, the worst-case minimax regret is $\widet…
Online Sub-Sampling for Reinforcement Learning with General Function Approximation
Dingwen Kong, Ruslan Salakhutdinov, Ruosong Wang +1
Most of the existing works for reinforcement learning (RL) with general function approximation (FA) focus on understanding the statistical complexity or regret bounds. However, the…
Instabilities of Offline RL with Pre-Trained Neural Representation
Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov +1
In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substanti…
What are the Statistical Limits of Offline RL with Linear Function Approximation?
Ruosong Wang, Dean P. Foster, Sham M. Kakade
Offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinf…
Planning with Submodular Objective Functions
Ruosong Wang, Hanrui Zhang, Devendra Singh Chaplot +2
We study planning with submodular objective functions, where instead of maximizing the cumulative reward, the goal is to maximize the objective value induced by a submodular functi…
On Reward-Free Reinforcement Learning with Linear Function Approximation
Ruosong Wang, Simon S. Du, Lin F. Yang +1
Reward-free reinforcement learning (RL) is a framework which is suitable for both the batch RL setting and the setting where there are many reward functions of interest. During the…
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?
Ruosong Wang, Simon S. Du, Lin F. Yang +1
Learning to plan for long horizons is a central challenge in episodic reinforcement learning problems. A fundamental question is to understand how the difficulty of the problem sca…
Tight Bounds for the Subspace Sketch Problem with Applications
Yi Li, Ruosong Wang, David P. Woodruff
In the subspace sketch problem one is given an matrix with bit entries, and would like to compress it in an arbitrary way to build a small space data…