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SODA: Self-Organizing Distinctive State Abstraction
Active from 2003 - 2007
A major current challenge in reinforcement learning research is to extend methods that work well on discrete, short-range, low-dimensional problems to continuous, highdiameter, high-dimensional problems, such as robot navigation using high-resolution sensors. Using SODA an robot in a continuous world can, with little prior knowledge of its sensorimotor system, environment, and task, improve task learning by first using a self-organizing feature map to develop a set of higher-level perceptual features while exploring using primitive, local actions. Then using those features, the agent can build a set of high-level actions that carry it between perceptually distinctive states in the environment. SODA combines a perceptual abstraction of the agent�’s sensory input into useful perceptual features, and a temporal abstraction of the agent�’s motor output into extended, high-level actions, thus reducing both the dimensionality and the diameter of the task.
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People
Jefferson Provost
Ph.D. Alumni
jefferson provost [at] gmail com
Publications
Self-Organizing Distinctive State Abstraction Using Options
Jefferson Provost, Benjamin J. Kuipers, and Risto Miikkulainen
In
Proceedings of the 7th International Conference on Epigenetic Robotics
, 2007.
2007
Developing navigation behavior through self-organizing distinctive state abstraction
Jefferson Provost, Benjamin J. Kuipers, and Risto Miikkulainen
Connection Science
, 18:159-172, 2006.
2006
Self-Organizing Perceptual and Temporal Abstraction for Robot Reinforcement Learning
Jefferson Provost, Benjamin J. Kuipers and Risto Miikkulainen
In
AAAI-04 Workshop on Learning and Planning in Markov Processes
, 2004.
2004
Toward Learning the Causal Layer of the Spatial Semantic Hierarchy using SOMs
Jefferson Provost, Patrick Beeson, and Benjamin J. Kuipers
In
AAAI Spring Symposium Series, Learning Grounded Representations
, 2001.
2001