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ABSTRACT Intrinsic motivation serves as the predominant paradigm of exploration in reinforcement learning. In pursuit of an informative and robust state representation, the behavioural metric groups behaviourally equival...

ABSTRACT As a government‐regulated public service, traffic signal control (TSC) requires reliable and transparent decision‐making. However, existing deep reinforcement learning (DRL) methods, despite improvements in ...

Abstract With the rapid adoption of multi-cloud platforms, dynamic orchestration of service function chains faces coupled challenges. This study proposes a hierarchical service chain orchestration for multi-cloud environ...

In the busy and stressful modern world, people tend to disregard mental health, still it is an important factor of overall health. The constant pressure to achieve success, the invasive nature of technology, and the cons...

Abstract Matching college students with appropriate innovative training projects is a challenging task that often relies on static assignment techniques, which overlook individual interests, skills, and learning styles. ...

The motion planning is a critical component of autonomous navigation, requiring the vehicle to reach a target location safely. Traditional navigation approaches for four-wheel differential drive automated guided vehicles...

Abstract With the outward expansion of university campuses toward suburban areas, the modern campus has evolved into an increasingly independent social space, providing an organizational setting in which users’ daily a...

The transportation sector is a significant contributor to global greenhouse gas emissions, and Electric Vehicles (EVs) have emerged as a promising solution to mitigate this impact by reducing emissions and integrating re...

Materias / palabras clave: Reinforcement learning; Computer science; Artificial intelligence; Variety (cybernetics); Deep learning; Control (management); Perception; Human–computer interaction; Machine learning; Neuroscience; Biology

Software agents are programs that can observe their environment and act in an attempt to reach their design goals. In most cases the selection of particular agent architecture determines the behaviour in response to the ...

"This thesis replicates and proposes an alternative method to train reinforcement learning algorithms with ErrP signals, captured through EEG, and validate the effectiveness of its use in a prototype application." Proyec...

Due to the safety risks and training sample inefficiency, it is often preferred to develop controllers in simulation. However, minor differences between the simulation and the real world can cause a significant sim-to-re...

Order acceptance under uncertainty is a critical decision-making problem at the interface between customer relationship management and production planning of order-driven manufacturing systems. In this work, a novel appr...

"Very few learning systems applied to problem solving have focused on learning operator definitions from the interaction with a completely unknown environment. In order to achieve better learning convergence, several age...

With the continuous innovation in household appliance technology and the improvement of living standards, the production of discarded household appliances has rapidly increased, making their recycling increasingly signif...

Adaptive learning systems require effective mechanisms to model students’ evolving learning states and provide personalized learning path recommendations. Traditional rule-based approaches are limited in capturing...