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 …

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 …

Exploration is a fundamental challenge in reinforcement learning (RL). Many current
exploration methods for deep RL use task-agnostic objectives, such as information gain or …

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 …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

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 …