A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …

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

Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong
assumptions on both the function classes (eg, Bellman-completeness) and the data …

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 …

Recent theoretical work studies sample-efficient reinforcement learning (RL) extensively in
two settings: learning interactively in the environment (online RL), or learning from an offline …

Actor-critic methods are widely used in offline reinforcement learningpractice, but are not so
well-understood theoretically. We propose a newoffline actor-critic algorithm that naturally …

This work studies the question of Representation Learning in RL: how can we learn a
compact low-dimensional representation such that on top of the representation we can …

Two central paradigms have emerged in the reinforcement learning (RL) community: online
RL and offline RL. In the online RL setting, the agent has no prior knowledge of the …

We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where
instead of receiving a numeric reward at each step, the RL agent only receives preferences …