Jefferson Provost, Benjamin J. Kuipers and Risto Miikkulainen. 2006.
Developing navigation behavior through self-organizing distinctive state abstraction.
Connection Science 18(2): 159-172, 2006.


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.