Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …

Deep learning proved its efficiency in many fields of computer science such as computer
vision, image classifications, object detection, image segmentation, and more. Deep …

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 …

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 …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional
reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of …

Intelligent creatures can explore their environments and learn useful skills without
supervision. In this paper, we propose DIAYN ('Diversity is All You Need'), a method for …

A fundamental problem in computer animation is that of realizing purposeful and realistic
human movement given a sufficiently-rich set of motion capture clips. We learn data-driven …

Achieving versatile robot locomotion requires motor skills that can adapt to previously
unseen situations. We propose a multi-expert learning architecture (MELA) that learns to …

Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …