256 citations · 627 across the 30 of their papers we have counts for
10 papers · 1 filter
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…
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Keyulu Xu, Mozhi Zhang, Jingling Li +3
We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical…
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…
-learning with Logarithmic Regret
Kunhe Yang, Lin F. Yang, Simon S. Du
This paper presents the first non-asymptotic result showing that a model-free algorithm can achieve a logarithmic cumulative regret for episodic tabular reinforcement learning if t…
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…
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
Fei Feng, Ruosong Wang, Wotao Yin +2
Motivated by the prevailing paradigm of using unsupervised learning for efficient exploration in reinforcement learning (RL) problems [tang2017exploration,bellemare2016unifying], w…