With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Data-driven methods have prominently featured in the progressive research and
development of modern condition monitoring systems for electrical machines. These …

Recent advancements in deep learning (DL) have posed a significant challenge for
automatic speech recognition (ASR). ASR relies on extensive training datasets, including …

Localization is a critical issue for many location-based applications in the Internet of
Underwater Things (IoUT). Nevertheless, the asynchronous time clock, stratification effect …

The development of Industry 4.0 (I4. 0) and the digitization and automation of manufacturing
processes have created a demand for designing smart warehouses to support …

A central capability of a long-lived reinforcement learning (RL) agent is to incrementally
adapt its behavior as its environment changes and to incrementally build upon previous …

Deep learning is an effective method for extracting the underlying information in text.
However, it performs better on closed datasets and is less effective in real-world scenarios …

For real-world deployments, it is critical to allow robots to navigate in complex environments
autonomously. Traditional methods usually maintain an internal map of the environment …

A reinforcement learning-based method is proposed for optimal sensor placement in the
spatial domain for modeling distributed parameter systems (DPSs). First, a low-dimensional …