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 …

Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on
prior experience. Gradient (or optimization) based meta-learning has recently emerged as …

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which
adapts a deep neural network on the edge-labeling graph, for few-shot learning. The …

Lack of performance when it comes to continual learning over non-stationary distributions of
data remains a major challenge in scaling neural network learning to more human realistic …