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 …

Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …

Despite the advancement of machine learning techniques in recent years, state-of-the-art
systems lack robustness to “real world” events, where the input distributions and tasks …

The advancement of continual reinforcement learning (RL) has been facing various
obstacles, including standardized metrics and evaluation protocols, demanding …

Continual Learning (CL, sometimes also termed incremental learning) is a flavor of machine
learning where the usual assumption of stationary data distribution is relaxed or omitted …

We propose RAPid-Learn (Learning to Recover and Plan Again), a hybrid planning and
learning method, to tackle the problem of adapting to sudden and unexpected changes in an …

Existing artificial intelligence (AI) agents are most successful on narrow, well-defined tasks,
where training data are plentiful, well-labeled, and match the deployment scenarios. It is …

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world
applications, it is important that they exhibit the ability to continually learn and adapt in …

We introduce a novel technique to address continual reinforcement learning (CRL), ie,
reinforcement learning (RL) in non-stationary environments. This requires agents to rapidly …

Deep RL became the state-of-the-art method to solve RL problems [1, 2]. From games to real-
life scenarios, Deep RL methods took precedence over other machine learning methods in …