Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …

While recent advances in deep reinforcement learning have allowed autonomous learning
agents to succeed at a variety of complex tasks, existing algorithms generally require a lot …

Interactive reinforcement learning (RL) has been successfully used in various applications in
different fields, which has also motivated HCI researchers to contribute in this area. In this …

While recent work in reinforcement learning (RL) has led to agents capable of solving
increasingly complex tasks, the issue of high sample complexity is still a major concern. This …

Designing intelligent agents that interact proficiently with people necessitates the prediction
of human decision-making. We present and discuss three prediction paradigms for …

In the future, robots may live with users as long-term companions, thus it is important that
some sort of attachment relationship develop between humans and agents. Man's best …

How should reinforcement learning (RL) agents explain themselves to humans not trained in
AI? To gain insights into this question, we conducted a 124-participant, four-treatment …

Providing Reinforcement Learning agents with expert advice can dramatically improve
various aspects of learning. Prior work has developed teaching protocols that enable agents …