Modern air defense confrontations demand rapid, precise task assignments in environments where threats evolve within seconds.

Abstract: This research article presents a comparison between two mainstream Deep Reinforcement Learning (DRL) algorithms, Asynchronous Advantage Actor-Critic (A3C) and Proximal Policy Optimization ...

This repository contains an implementation of a Deep Reinforcement Learning (DRL) algorithm for managing the energy demand and supply of a microgrid. The implementation is built using Python and is ...

Abstract: An energy management system is crucial for optimizing the performance and reducing fuel consumption of Plug-in Hybrid Electric Vehicles (PHEVs), which plays an important role in sustainable ...

Reinforcement learning (RL) is a type of artificial intelligence. In RL, a system learns to make choices by interacting with its environment. This method involves experimentation. The system receives ...

The combustion process of boilers under deep peak shaving is a multivariate process which has complex characteristics such as super multivariability, being nonlinear, and large delay. It is difficult ...

The uncertainty of renewable energy and demand response brings many challenges to the microgrid energy management. Driven by the recent advances and applications of deep reinforcement learning a ...

Swimming microorganisms and migrating cells have developed various strategies in order to move in nutrient-rich or other chemical environments. We apply a genetic algorithm to the internal ...