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 …

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 …

In learning an embodied agent executing daily tasks via language directives, the literature
largely assumes that the agent learns all training data at the beginning. We argue that such …

The processes of information processing in the form of knowledge are at the forefront when
considering the educational social and communication environment as a holistic system. In …

Abstract Although Reinforcement Learning (RL) algorithms have made impressive progress
in learning complex tasks over the past years, there are still prevailing short-comings and …

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

In this article, we present a cognitive architecture that is built from powerful yet simple neural
models. Specifically, we describe an implementation of the common model of cognition …

Wang's definition of Artificial Intelligence is developed via careful and thorough abstractions
from human intelligence. Motivated by the goal of building a definition that will be genuinely …

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 …