We present BRIEE, an algorithm for efficient reinforcement learning in Markov Decision
Processes with block-structured dynamics (ie, Block MDPs), where rich observations are …

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 …

Offline reinforcement learning (RL) aims to find an optimal policy for sequential decision-
making using a pre-collected dataset, without further interaction with the environment …

We consider a challenging theoretical problem in offline reinforcement learning (RL):
obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

Sample-efficient offline reinforcement learning (RL) with linear function approximation has
been studied extensively recently. Much of the prior work has yielded instance-independent …

We consider a contextual bandit problem with $ S $ contexts and $ K $ actions. In each
round $ t= 1, 2,\dots $ the learnerobserves a random context and chooses an action based …

While multitask representation learning has become a popular approach in reinforcement
learning (RL) to boost the sample efficiency, the theoretical understanding of why and how it …

We study the problem of representation learning in stochastic contextual linear bandits.
While the primary concern in this domain is usually to find\textit {realizable} representations …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …