In this paper, we consider federated reinforcement learning for tabular episodic Markov
Decision Processes (MDP) where, under the coordination of a central server, multiple …

The exploration-exploitation dilemma has been a central challenge in reinforcement
learning (RL) with complex model classes. In this paper, we propose a new algorithm …

A crucial problem in reinforcement learning is learning the optimal policy. We study this in
tabular infinite-horizon discounted Markov decision processes under the online setting. The …

This article reviews the recent advances on the statistical foundation of reinforcement
learning (RL) in the offline and low-adaptive settings. We will start by arguing why offline RL …

We study stochastic delayed feedback in general single-agent and multi-agent sequential
decision making, which includes bandits, single-agent Markov decision processes (MDPs) …

We study the problem of deployment efficient reinforcement learning (RL) with linear
function approximation under the\emph {reward-free} exploration setting. This is a well …

In many real-life reinforcement learning (RL) problems, deploying new policies is costly. In
those scenarios, algorithms must solve exploration (which requires adaptivity) while …

We theoretically explore the relationship between sample-efficiency and adaptivity in
reinforcement learning. An algorithm is sample-efficient if it uses a number of queries $ n …

Differential privacy (DP) has recently been introduced into episodic reinforcement learning
(RL) to formally address user privacy concerns in personalized services. Previous work …

We study the problem of multi-agent reinforcement learning (MARL) with adaptivity
constraints--a new problem motivated by real-world applications where deployments of new …