Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Model-based reinforcement learning methods often use learning only for the purpose of
estimating an approximate dynamics model, offloading the rest of the decision-making work …

Constructing agents with planning capabilities has long been one of the main challenges in
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …

Designing effective model-based reinforcement learning algorithms is difficult because the
ease of data generation must be weighed against the bias of model-generated data. In this …

This paper studies model-based reinforcement learning (RL) for regret minimization. We
focus on finite-horizon episodic RL where the transition model $ P $ belongs to a known …

In order to deal with the curse of dimensionality in reinforcement learning (RL), it is common
practice to make parametric assumptions where values or policies are functions of some low …

The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge
of building AI agents with general competency across dozens of Atari 2600 games. It …

We study the sample complexity of model-based reinforcement learning (henceforth RL) in
general contextual decision processes that require strategic exploration to find a near …

The low-rank MDP has emerged as an important model for studying representation learning
and exploration in reinforcement learning. With a known representation, several model-free …