The path planning issue of the underactuated autonomous underwater vehicle (AUV) under
ocean current disturbance is studied in this paper. In order to improve the AUV's path …

In this paper we consider deterministic policy gradient algorithms for reinforcement learning
with continuous actions. The deterministic policy gradient has a particularly appealing form …

Integrated hydrogen energy systems (IHESs) have attracted extensive attention in mitigating
climate problems. As a kind of large‐scale hydrogen storage device, underground hydrogen …

Traffic congestion has been an actual problem in large cities, causing personal
inconvenience and environmental pollution. To solve this problem, new applications for …

This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an
OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to …

Given that transportation contributes to 23% of global energy-related greenhouse gas
emissions, the electrification of the transport sector is an inevitable trend. This paper …

With the rise of machine learning, a lot of excellent algorithms are used for settlement
prediction. Backpropagation (BP) and Elman are two typical algorithms based on gradient …

Discrete event modeling and simulation and reinforcement learning are two frameworks
suited for cyberphysical system design, which, when combined, can give powerful tools for …

With the development of new energy technology, the distributed generation has attracted
more and more attention. In order to enhance the inertia of distributed generator system to …

The traditional deep deterministic policy gradient (DDPG) algorithm has the disadvantages
of slow convergence velocity and ease of falling into the local optimum. From these two …