Offline or batch reinforcement learning seeks to learn a near-optimal policy using history
data without active exploration of the environment. To counter the insufficient coverage and …

We study reinforcement learning (RL) with linear function approximation where the
underlying transition probability kernel of the Markov decision process (MDP) is a linear …

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 © …

Episodic reinforcement learning and contextual bandits are two widely studied sequential
decision-making problems. Episodic reinforcement learning generalizes contextual bandits …

Recent studies have shown that episodic reinforcement learning (RL) is not more difficult
than bandits, even with a long planning horizon and unknown state transitions. However …

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 …

Achieving sample efficiency in online episodic reinforcement learning (RL) requires
optimally balancing exploration and exploitation. When it comes to a finite-horizon episodic …

Abstract Multi-distribution learning (MDL), which seeks to learn a shared model that
minimizes the worst-case risk across $ k $ distinct data distributions, has emerged as a …

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 …

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 …