We study federated contextual linear bandits, where $ M $ agents cooperate with each other
to solve a global contextual linear bandit problem with the help of a central server. We …

A central problem in sequential decision making is to develop algorithms that are practical
and computationally efficient, yet support the use of flexible, general-purpose models …

In some applications of reinforcement learning, a dataset of pre-collected experience is
already availablebut it is also possible to acquire some additional online data to help …

A fundamental question in the theory of reinforcement learning is: suppose the optimal $ Q $-
function lies in the linear span of a given $ d $ dimensional feature map**, is sample …

We study reinforcement learning (RL) with linear function approximation under the adaptivity
constraint. We consider two popular limited adaptivity models: the batch learning model and …

We study how representation learning can improve the efficiency of bandit problems. We
study the setting where we play $ T $ linear bandits with dimension $ d $ concurrently, and …

In this paper, we study the episodic reinforcement learning (RL) problem modeled by finite-
horizon Markov Decision Processes (MDPs) with constraint on the number of batches. The …

In this paper, we provide the first efficient batched algorithm for contextual linear bandits with
large action spaces. Unlike existing batched algorithms that rely on action elimination, which …

We study the problem of experiment planning with function approximation in contextual
bandit problems. In settings where there is a significant overhead to deploying adaptive …

We study multi-agent reinforcement learning in the setting of episodic Markov decision
processes, where many agents cooperate via communication through a central server. We …