37 citations · 100 across the 13 of their papers we have counts for
4 papers · 1 filter
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…