Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

Reinforcement Learning (RL) has emerged as a highly effective technique for addressing
various scientific and applied problems. Despite its success, certain complex tasks remain …

Off-policy deep reinforcement learning (RL) has been successful in a range of challenging
domains. However, standard off-policy RL algorithms can suffer from several issues, such as …

In this paper, a practical machine learning-based fault diagnosis method is proposed for
induction motors using experimental data. Various single-and multi-electrical and/or …

In some stochastic environments the well-known reinforcement learning algorithm Q-
learning performs very poorly. This poor performance is caused by large overestimations of …

Reinforcement learning offers a promising methodology for developing skills for simulated
characters, but typically requires working with sparse hand-crafted features. Building on …

With accelerated ensemble of the Internet of Things technology and automotive industry,
vehicular network has been established as powerful tools. However, it is a significant …

In reinforcement learning (RL), balancing exploration and exploitation is crucial for
achieving an optimal policy in a sample-efficient way. To this end, existing sample-efficient …

In this article, we introduce some recent research trends within the field of
adaptive/approximate dynamic programming (ADP), including the variations on the structure …

The recent advances in computing technologies and the increasing availability of large
amounts of data in smart grids and smart cities are generating new research opportunities in …