Planning for Novelty: Width-Based Algorithms for Common Problems in
  Control, Planning and Reinforcement Learning
  Width-based algorithms search for solutions through a general definition of
state novelty. These algorithms have been shown to result in state-of-the-art
performance in classical planning, and have been successfully applied to
model-based and model-free settings where the dynamics of the problem are given
through simulation engines. Width-based algorithms performance is understood
theoretically through the notion of planning width, providing polynomial
guarantees on their runtime and memory consumption. To facilitate synergies
across research communities, this paper summarizes the area of width-based
planning, and surveys current and future research directions.
