Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

We study offline reinforcement learning (RL), which aims to learn an optimal policy based on
a dataset collected a priori. Due to the lack of further interactions with the environment …

A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

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

Partial observability is ubiquitous in applications of Reinforcement Learning (RL), in which
agents learn to make a sequence of decisions despite lacking complete information about …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Abstract Reinforcement learning from Human Feedback (RLHF) learns from preference
signals, while standard Reinforcement Learning (RL) directly learns from reward signals …

In order to deal with the curse of dimensionality in reinforcement learning (RL), it is common
practice to make parametric assumptions where values or policies are functions of some low …

This paper studies systematic exploration for reinforcement learning (RL) with rich
observations and function approximation. We introduce contextual decision processes …

This paper studies the theoretical framework of the alignment process of generative models
with Reinforcement Learning from Human Feedback (RLHF). We consider a standard …