Almost all the work in Average-reward Reinforcement Learning (ARL) so far has fo-cused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a model-based ARL method called H-learning to address the scale-up problem. We extend H-learning to learn action models and reward functions in the form of Bayesian networks, and approxima...

This paper describes a concept for measuring the reproducible performance of mobile manipulators to be used for assembly or other similar tasks. An automatic guided vehicle with an onboard robot arm was programmed to repeatedly move to and stop at a novel, reconfigurable mobile manipulator artifact (RMMA), sense the RMMA, and detect targets on the RMMA. The manipulator moved a laser retroreflec...