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@Article{provost-connsci-05,
  author = 	 {Jefferson Provost and Benjamin J. Kuipers and Risto Miikkulainen},
  title = 	 {Developing navigation behavior through self-organizing distinctive state abstraction},
  journal = 	 {Connection Science},
  year = 	 {2006},
  volume =	 18,
  number =	 2,
  abstract = {A major challenge in reinforcement learning research is to extend methods that have worked well on discrete, short-range, low-dimensional problems to continuous, high-diameter, high-dimensional problems, such as robot navigation using high-resolution sensors.  Self-Organizing Distinctive-state Abstraction (SODA) is a new, generic method by which a robot in a continuous world can better learn to navigate by learning a set of high-level features and building temporally-extended actions to carry it between distinctive states based on those features.  A SODA agent first uses a self-organizing feature map to develop a set of high-level perceptual features while exploring the environment with primitive, local actions. The agent then builds a set of high-level actions composed of generic trajectory-following and hill-climbing control laws that carry it between the states at local maxima of feature activations.  In an experiment on a simulated robot navigation task, the SODA agent learns to perform a task requiring 300 small-scale, local actions using as few as 9 new, temporally-extended actions, significantly improving learning time over navigating with the local actions.}
}