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Abstract The new energy vehicle plays a crucial role in green transportation, and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving. This paper presents a state-of-...

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad...

An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcemen...

Materias / palabras clave: Reinforcement learning; Computer science; Reinforcement; Artificial intelligence; Machine learning; Psychology; Social psychology

Materias / palabras clave: Reinforcement learning; Computer science; Reinforcement; Artificial intelligence; Engineering; Structural engineering

The increasing penetration of renewable energy sources (RES) reduces power system inertia, causing severe frequency excursions following disturbances. Conventional Automatic Generation Control (AGC) using fixed-gain cont...

Introduction. In recent days, electric vehicles, robotics and in many control system applications, permanent magnet synchronous motors (PMSMs) are widely utilized. Problem. Due to non-linear behavior of system, external ...

Abstract Semiconductor lithography, a pivotal process in integrated circuit (IC) fabrication, accounts for approximately 30% of production costs and faces significant challenges as feature sizes shrink to sub-nanometer s...

Abstract Individualized educational management cannot be avoided in meeting various learner needs in cases where teaching strategies are changed in line with the performance trends and preferences of students. Reinforcem...

ABSTRACT Background and Aims De novo drug design is the process of generating novel lead compounds that possess desirable pharmacological activities and optimal physicochemical properties for therapeutic development. In ...

Abstract The deep reinforcement learning techniques offers a novel approach to real-time control for hybrid power flow controllers. This paper introduces a bus voltage optimization adjustment strategy, utilizing a Markov...

To achieve high accuracies in deep learning-based inspection of IC markings, a large number of manually labeled images of good and defective markings are needed. In many IC production lines, only a handful of labeled ima...

Human driven vehicles (HDVs) with selfish objectives cause low traffic efficiency in an un-signalized intersection. On the other hand, autonomous vehicles can overcome this inefficiency through perfect coordination. In t...

Efficient UAV path planning in large and dynamic environments requires adaptive strategies that account for varying regional importance. Traditional coverage or Reinforcement Learning-based methods often suffer from redu...

Abstract Multimodal industrial documents–such as operation manuals, circuit diagrams, and parameter tables–contain domain knowledge distributed across text, images, and document layout. However, most existing retriev...

he orchestration of microservices in distributed cloud environments poses significant challenges due to the heterogeneous nature of cluster nodes and dynamic workload patterns. Traditional scheduling strategies in Kubern...

In recent years, the advancement of artificial intelligence techniques has led to significant interest in reinforcement learning (RL) within the traffic and transportation community. Dynamic traffic control has emerged a...

This paper proposes an innovative model-free deep reinforcement learning-based controller (RL-C) for a grid-connected 5-level packed-U-cell (PUC5) multilevel inverter (MLI). The controller is designed to deliver a high-q...

Abstract Inventory management is a core part of supply chains, and over the years it has been increasingly challenged by the need to balance economic performance with environmental considerations. While prior reinforceme...

ABSTRACT This paper presents HealthNet, a novel framework for the dynamic optimisation of healthcare transportation networks using multi‐agent reinforcement learning. HealthNet leverages a spatiotemporal dependency mod...

Autonomous microrobotic navigation in life sciences is highly challenging due to environmental disturbances, unpredictable phenomena, diverse obstacles, and dynamic changes. These significantly limit motion of microrobot...

Abstract To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making, this study proposes a hybrid framework to combine deep re...

The evolution of metropolitan cities and increasing travel demand impose stringent requirements on traffic assignment methods. Multi-agent reinforcement learning (MARL) approaches outperform traditional methods in modeli...