Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent
years and are considered by both academia and industry to be key enablers for Industry 4.0 …

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 …

Learning goal-directed behavior in environments with sparse feedback is a major challenge
for reinforcement learning algorithms. One of the key difficulties is insufficient exploration …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …

In off-policy deep reinforcement learning, it is usually hard to collect sufficient successful
experiences with sparse rewards to learn from. Hindsight experience replay (HER) enables …

This paper introduces a machine learning based system for controlling a robotic manipulator
with visual perception only. The capability to autonomously learn robot controllers solely …

Current robotic studies are focused on the performance of specific tasks. However, such
tasks cannot be generalized, and some special tasks, such as compliant and precise …

Infants are experts at playing, with an amazing ability to generate novel structured behaviors
in unstructured environments that lack clear extrinsic reward signals. We seek to …

Learning complex locomotion and manipulation tasks presents significant challenges, often
requiring extensive engineering of, eg, reward functions or curricula to provide meaningful …