We propose a deep stochastic actor–critic algorithm with an integrated network architecture
and fewer parameters. We address stabilization of the learning procedure via an adaptive …

Electricity authorities need capacity assessment and expansion plans for efficiently charging
the growing Electric vehicle (EV) fleet. Specifically, the distribution grid needs significant …

Neural networks play an increasingly important role in many complex decision-making
systems. However, their lack of interpretability make it difficult to analyze and trust them …

This paper presents a novel method for power system reliability studies that combines graph
neural networks with reinforcement learning. Monte Carlo methods are the backbone of …

Due to the rapid development of hardware devices, the analytical processing and
algorithmic capabilities of computers are also being enhanced, which makes machine …

Optimization, which refers to making the best or most out of a system, is critical for an
organization's strategic planning. Optimization theories and techniques aim to find the …

Nowadays, with the development of science and technology, control objects are increasingly
complex, have high nonlinearities and large uncertainties, making traditional classic control …