This paper investigates model robustness in reinforcement learning (RL) via the framework
of distributionally robust Markov decision processes (RMDPs). Despite recent efforts, the …

Settling the sample complexity of model-based offline reinforcement learning Page 1 The
Annals of Statistics 2024, Vol. 52, No. 1, 233–260 https://doi.org/10.1214/23-AOS2342 © …

This paper is concerned with the asynchronous form of Q-learning, which applies a
stochastic approximation scheme to Markovian data samples. Motivated by the recent …

A central issue lying at the heart of online reinforcement learning (RL) is data efficiency.
While a number of recent works achieved asymptotically minimal regret in online RL, the …

We study the problem of agnostic PAC reinforcement learning (RL): given a policy class $\Pi
$, how many rounds of interaction with an unknown MDP (with a potentially large state and …

This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes
access to both an offline dataset and online interactions with the unknown environment. A …

A/B testing is critical for modern technological companies to evaluate the effectiveness of
newly developed products against standard baselines. This paper studies optimal designs …

Experience replay is an indispensable part of deep reinforcement learning algorithms that
enables the agent to revisit and reuse its past and recent experiences to update the network …

Inverse Reinforcement Learning (IRL)---the problem of learning reward functions from
demonstrations of an\emph {expert policy}---plays a critical role in develo** intelligent …

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 …