Artificial intelligence research has seen enormous progress over the past few decades, but it
predominantly relies on fixed datasets and stationary environments. Continual learning is an …

A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …

In a standard view of the reinforcement learning problem, an agent's goal is to efficiently
identify a policy that maximizes long-term reward. However, this perspective is based on a …

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 …

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks
in which the agent has only limited environmental feedback. Despite many advances over …

In lifelong learning, the learner is presented with a sequence of tasks, incrementally building
a data-driven prior which may be leveraged to speed up learning of a new task. In this work …

Interacting with a complex world involves continual learning, in which tasks and data
distributions change over time. A continual learning system should demonstrate both …

The ability to learn continually without forgetting the past tasks is a desired attribute for
artificial learning systems. Existing approaches to enable such learning in artificial neural …

Humans and animals have the ability to continually acquire, fine-tune, and transfer
knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is …

In continual learning (CL), an agent learns from a stream of tasks leveraging prior
experience to transfer knowledge to future tasks. It is an ideal framework to decrease the …