Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Smart agriculture systems based on Internet of Things are the most promising to increase
food production and reduce the consumption of resources like fresh water. In this study, we …

Driven by the recent technological advancements within the field of artificial intelligence
research, deep learning has emerged as a promising representation learning technique …

We address the problem of learning structured policies for continuous control. In traditional
reinforcement learning, policies of agents are learned by MLPs which take the …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

With the success of deep learning in a wide variety of areas, many deep multi-task learning
(MTL) models have been proposed claiming improvements in performance obtained by …