We introduce the first sample-efficient algorithm for LMDPs without any additional distributional assumptions . Our result builds off a new perspective on the role of off-policy evaluation guarantees and coverage coefficients in LMDPs, a perspective, that has been overlooked in the context of exploration in partially observed environments.

This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.

When we want to understand how matter behaves, the real action happens at the atomic scale. Heating of water, a chemical reaction in a battery, the way proteins fold in our cells, or how a catalyst works to convert carbon dioxide into useful fuels, all of these processes are governed by the motions and interactions of atoms. Atomic-scale simulations give us a way to explore the microscopic behavior of matter, by tracking how atoms move under the laws of quantum mechanics. These simulations have become essential across physics, chemistry, biology, and materials science. They test hypotheses that experiments cannot easily probe and help design new materials before they are synthesized and tested in the lab.

A general idea to overcome the above problem is to perturb the system dynamics.

RL achieves the optimal average cost while incurring the least regret.

In this paper, we propose an efficient algorithm to accelerate computing the TT decomposition with the Alternating Least Squares (ALS) algorithm relying on exact leverage scores sampling.

We revisit this scenario as an experimental case study in Section 6.