Interactive reinforcement learning (IRL) has become an important apprenticeship approach
to speed up convergence in classic reinforcement learning (RL) problems. In this regard, a …

The main purpose of this paper is to present a novel algorithmic reinforcement learning (RL)
method for dam** the voltage and frequency oscillations in a micro-grid (MG) with …

Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an
agent to learn through experience acquired by interaction with its environment. Its potential …

Improving maintenance scheduling has become an area of crucial importance in recent
years. Condition-based maintenance (CBM) has started to move away from scheduled …

Designing a spoken dialogue system can be a time-consuming and challenging process. A
developer may spend a lot of time and effort anticipating the potential needs of a specific …

In this paper we investigate and develop a real-world reinforcement learning approach to
autonomously recharge a humanoid Nao robot [1]. Using a supervised reinforcement …

Autonomous systems able to react and change their behaviour in response to events during
operation. These established abilities are based on the preprogrammed action or actions to …

“Theory and Applications of Natural Language Processing” is a series of volumes dedicated
to selected topics in NLP and Language Technology. It focuses on the most recent advances …

The primary aim of this chapter is the design and application of intelligent methods based on
reinforcement learning (RL) for adaptive and online controlling the hybrid microgrids …

Current research on time series generation frequently depends on oversimplified data and
lenient evaluation methods, making it challenging to apply these models effectively in real …