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 …

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 …

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 …

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 …

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

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

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics
generates large datasets comprising hundreds to thousands of metabolites with complex …

Transfer in reinforcement learning is a novel research area that focuses on the development
of methods to transfer knowledge from a set of source tasks to a target task. Whenever the …

Much of reinforcement learning theory is built on top of oracles that are computationally hard
to implement. Specifically for learning near-optimal policies in Partially Observable Markov …

Policy gradient algorithms have shown considerable recent success in solving high-
dimensional sequential decision making tasks, particularly in robotics. However, these …