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 …

We study reinforcement learning (RL) with linear function approximation. For episodic time-
inhomogeneous linear Markov decision processes (linear MDPs) whose transition …

The proximal policy optimization (PPO) algorithm stands as one of the most prosperous
methods in the field of reinforcement learning (RL). Despite its success, the theoretical …

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 …

We study the problem of reinforcement learning (RL) with low (policy) switching cost {—} a
problem well-motivated by real-life RL applications in which deployments of new policies …

We study a Markov matching market involving a planner and a set of strategic agents on the
two sides of the market. At each step, the agents are presented with a dynamical context …

In today's economy, it becomes important for Internet platforms to consider the sequential
information design problem to align its long term interest with incentives of the gig service …

We study the stochastic shortest path (SSP) problem in reinforcement learning with linear
function approximation, where the transition kernel is represented as a linear mixture of …

We study the off-policy evaluation (OPE) problem in reinforcement learning with linear
function approximation, which aims to estimate the value function of a target policy based on …

Most of the existing works for reinforcement learning (RL) with general function
approximation (FA) focus on understanding the statistical complexity or regret bounds …