The Chef's Hat Simulation Environment for Reinforcement-Learning-Based
  Agents
  To achieve social interactions within Human-Robot Interaction (HRI)
environments is a very challenging task. Most of the current research focuses
on Wizard-of-Oz approaches, which neglect the recent development of intelligent
robots. On the other hand, real-world scenarios usually do not provide the
necessary control and reproducibility which are needed for learning algorithms.
In this paper, we propose a virtual simulation environment that implements the
Chef's Hat card game, designed to be used in HRI scenarios, to provide a
controllable and reproducible scenario for reinforcement-learning algorithms.
