Finding the minimal structural assumptions that empower sample-efficient learning is one of
the most important research directions in Reinforcement Learning (RL). This paper …

A major challenge in reinforcement learning is to develop practical, sample-efficient
algorithms for exploration in high-dimensional domains where generalization and function …

The low-rank MDP has emerged as an important model for studying representation learning
and exploration in reinforcement learning. With a known representation, several model-free …

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 …

Modern reinforcement learning (RL) commonly engages practical problems with large state
spaces, where function approximation must be deployed to approximate either the value …

In the classical multi-armed bandit problem, instance-dependent algorithms attain improved
performance on" easy" problems with a gap between the best and second-best arm. Are …

The classical theory of reinforcement learning (RL) has focused on tabular and linear
representations of value functions. Further progress hinges on combining RL with modern …

This paper considers two-player zero-sum finite-horizon Markov games with simultaneous
moves. The study focuses on the challenging settings where the value function or the model …

Thompson Sampling is one of the most effective methods for contextual bandits and has
been generalized to posterior sampling for certain MDP settings. However, existing posterior …

We study function approximation for episodic reinforcement learning with entropic risk
measure. We first propose an algorithm with linear function approximation. Compared to …