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 …

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 © …

Since reinforcement learning algorithms are notoriously data-intensive, the task of sampling
observations from the environment is usually split across multiple agents. However …

We investigate the sample efficiency of reinforcement learning in a $\gamma $-discounted
infinite-horizon Markov decision process (MDP) with state space S and action space A …

Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP)
in a model-free fashion, lies at the heart of reinforcement learning. When it comes to the …

In this paper, we consider federated Q-learning, which aims to learn an optimal Q-function
by periodically aggregating local Q-estimates trained on local data alone. Focusing on …

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 …

We consider a reinforcement learning setting in which the deployment environment is
different from the training environment. Applying a robust Markov decision processes …

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