Critical reinforcement materials will enhance initiatives for IC substrates and semiconductor packaging technologies.

Trustworthy AI isn’t just about predicting the right outcome; it’s about knowing how confident we should actually be.

Abstract: To date, distributional reinforcement learning (distributional RL) methods have exclusively focused on the discounted setting, where an agent aims to optimize a discounted sum of rewards ...

Abstract: Recently, the automated differential trail search methods are well knowns. Especially for Ascon, using constraint-solving techniques such as MILP, SAT, and CP, which encode mathematical ...

The integration of deep reinforcement learning with PD control in humanoid robots enhances gait stability and patient comfort during lower limb rehabilitation.

Negative reinforcement has a bad reputation. Here’s what it really means, and why it can be surprisingly helpful.

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have an important impact. That may feel especially true, for example, when ...

Abstract: Heavy-lift electric vertical take-off and landing aircraft (eVTOL) play a vital role in advancing the low-altitude economy by enabling the transportation of high payloads in dense urban ...

A reproducible implementation of reinforcement learning algorithms for robotic manipulation tasks. This project focuses on continuous control problems using state-of-the-art deep RL methods. This is a ...