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 …

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …

Reliability of reinforcement learning (RL) agents is a largely unsolved problem. Especially in
situations that substantially differ from their training environment, RL agents often exhibit …

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 …

Anticipatory thinking is necessary for managing risk in the safety‐and mission‐critical
domains where AI systems are being deployed. We analyze the intersection of anticipatory …

A robust body of reinforcement learning techniques have been developed to solve complex
sequential decision making problems. However, these methods assume that train and …

Abstract “Open world” environments are those in which novel objects, agents, events, and
more can appear and contradict previous understandings of the environment. This runs …

Abstract Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that
focuses on methods for solving off-nominal, or anomalous problems in autonomous …

Abstract Model-based reasoning agents are ill-equipped to act in novel situations in which
their model of the environment no longer sufficiently represents the world. We propose …

Due to the emergence of AI systems that interact with the physical environment, there is an
increased interest in incorporating physical reasoning capabilities into those AI systems. But …