Relational Reinforcement Learning in Infinite Mario
  Relational representations in reinforcement learning allow for the use of
structural information like the presence of objects and relationships between
them in the description of value functions. Through this paper, we show that
such representations allow for the inclusion of background knowledge that
qualitatively describes a state and can be used to design agents that
demonstrate learning behavior in domains with large state and actions spaces
such as computer games.
