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 …

Deep reinforcement learning (RL) has become one of the most popular topics in artificial
intelligence research. It has been widely used in various fields, such as end-to-end control …

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 …

Deep reinforcement learning algorithms require large amounts of experience to learn an
individual task. While meta-reinforcement learning (meta-RL) algorithms can enable agents …

Foundation models have shown impressive adaptation and scalability in supervised and self-
supervised learning problems, but so far these successes have not fully translated to …

Foundation models have shown impressive adaptation and scalability in supervised and self-
supervised learning problems, but so far these successes have not fully translated to …

We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-
overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model …

Trading off exploration and exploitation in an unknown environment is key to maximising
expected return during learning. A Bayes-optimal policy, which does so optimally, conditions …

Consider the following instance of the Offline Meta Reinforcement Learning (OMRL)
problem: given the complete training logs of $ N $ conventional RL agents, trained on $ N …