In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

Most current decision-making research focuses on classical economic scenarios, where
choice offers are prespecified and where action dynamics play no role in the decision …

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 …

Reinforcement learning algorithms usually assume that all actions are always available to
an agent. However, both people and animals understand the general link between the …

As a popular concept proposed in the field of psychology, affordance has been regarded as
one of the important abilities that enable humans to understand and interact with the …

Robots are extending their presence in domestic environments every day, it being more
common to see them carrying out tasks in home scenarios. In the future, robots are expected …

Research on humanoid robotic systems involves a considerable amount of computational
resources, not only for the involved design but also for its development and subsequent …

Robotic systems are more present in our society everyday. In human–robot environments, it
is crucial that end-users may correctly understand their robotic team-partners, in order to …

A long-term goal of reinforcement learning agents is to be able to perform tasks in complex
real-world scenarios. The use of external information is one way of scaling agents to more …

Interactive reinforcement learning methods utilise an external information source to evaluate
decisions and accelerate learning. Previous work has shown that human advice could …