Abstract: Driven by advances in artificial intelligence, deep reinforcement learning (DRL) has made remarkable strides in adaptive traffic signal control (ATSC), empowering improved handling of ...

In this study, we focus on investigating a nonsmooth convex optimization problem involving the l 1-norm under a non-negative constraint, with the goal of developing an inverse-problem solver for image ...

In what is expected to be his final public remarks before the Federal Reserve’s October rate decision, Chair Jerome Powell said there is no "risk-free" path for policy, as the labor market shows signs ...

A modular, cross-platform Proximal Policy Optimization (PPO) implementation that can be integrated into JavaScript SPAs, Node.js apps, Unity 3D games, Python applications, and more. The system uses a ...

How much help is too much, and how little is too little? Supporting middle school students within their zone of proximal development (ZPD), a concept introduced by Lev Vygotsky, means identifying the ...

Reinforcement learning (RL) plays a crucial role in scaling language models, enabling them to solve complex tasks such as competition-level mathematics and programming through deeper reasoning.

ABSTRACT: The growing demand for energy-efficient Wireless Sensor Networks (WSNs) in applications such as IoT, environmental monitoring, and smart cities has sparked exhaustive research into practical ...

Olivera Ciraj Bjelac, IAEA Department of Nuclear Sciences and Applications To support hospitals and specialists around the world in meeting their safety standards requirements, the IAEA has produced a ...

This project implements a Proximal Policy Optimization (PPO) algorithm to train agents in OpenAI Gym environments. It includes modular support for environment configuration, checkpointing, and ...

ABSTRACT: Accurate prediction of stock prices remains a fundamental challenge in financial markets, with substantial implications for investment strategies and decision making. Although machine ...