Learning Factored Markov Decision Processes with Unawareness
  Methods for learning and planning in sequential decision problems often
assume the learner is aware of all possible states and actions in advance. This
assumption is sometimes untenable. In this paper, we give a method to learn
factored markov decision problems from both domain exploration and expert
assistance, which guarantees convergence to near-optimal behaviour, even when
the agent begins unaware of factors critical to success. Our experiments show
our agent learns optimal behaviour on small and large problems, and that
conserving information on discovering new possibilities results in faster
convergence.
