During Fusion 2019 Conference (https://www. fusion2019. org/program. html), leading
experts presented ideas on the historical, contemporary, and future coordination of artificial …

In this paper, we provide an overview of the existing methods for integrating human advice
into a Reinforcement Learning process. We first propose a taxonomy of the different forms of …

This paper investigates how to efficiently transition and update policies, trained initially with
demonstrations, using off-policy actor-critic reinforcement learning. It is well-known that …

While current autonomous navigation systems allow robots to successfully drive themselves
from one point to another in specific environments, they typically require extensive manual …

In order to deploy robots that could be adapted by non-expert users, interactive imitation
learning (IIL) methods must be flexible regarding the interaction preferences of the teacher …

The successful integration of autonomous robots in real-world environments strongly
depends on their ability to reason from context and take socially acceptable actions. Current …

This paper investigates how to utilize different forms of human interaction to safely train
autonomous systems in realtime by learning from both human demonstrations and …

Real-world tasks of interest are generally poorly defined by human-readable descriptions
and have no pre-defined reward signals unless it is defined by a human designer …

Recent progress in AI and Reinforcement learning has shown great success in solving
complex problems with high dimensional state spaces. However, most of these successes …

The modern world is evolving rapidly, especially with respect to the development and
proliferation of increasingly intelligent, artificial intelligence (AI) and AI-related technologies …