Electric vehicles are widely adopted globally as a sustainable mode of transportation. With
the increased availability of onboard computation and communication capabilities, vehicles …

Antibiotic resistance (AR) is considered one of the greatest global threats in the current
century, which can only be overcome if all interconnected areas of humans, animals and the …

Wind farm layout optimization is a challenging issue which demands to discover some trade-
off solutions considering various criteria, such as the power generated and the cost of the …

Robot navigation in crowded environments has recently benefited from advances in deep
reinforcement learning (DRL) approaches. However, it still presents a challenge to …

Multi-agent deep reinforcement learning (MADRL) approaches are at the forefront of
contemporary research in optimum electric vehicle (EV) charging scheduling challenges …

An energy management strategy (EMS) based on reinforcement learning is proposed in this
study to enhance the fuel economy and durability of a fuel cell hybrid bus (FCHB). Firstly, a …

Implementing a household integrated demand response (HIDR) can be an effective solution
to save energy and reduce carbon emissions in household multi-energy system (HMES) …

A stable and efficient path planning algorithm can improve the accuracy and efficiency of
sediment sampling process. To determine a path that can guide the sampler to the preset …

In this study, we present an innovative approach to robotic deburring path planning by
combining deep reinforcement learning (DRL) with an inverse Jacobian strategy. Existing …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …