Reinforcement-driven shaping of sequence learning in neural dynamics

Matthew Luciw, Sohrob Kazerounian, Yulia Sandamirskaya, Gregor Schöner, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present here a simulated model of a mobile Kuka Youbot which makes use of Dynamic Field Theory for its underlying perceptual and motor control systems, while learning behavioral sequences through Reinforcement Learning. Although dynamic neural fields have previously been used for robust control in robotics, high-level behavior has generally been pre-programmed by hand. In the present work we extend a recent framework for integrating reinforcement learning and dynamic neural fields, by using the principle of shaping, in order to reduce the search space of the learning agent. © 2014 Springer International Publishing Switzerland.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages12
ISBN (Print)9783319088631
StatePublished - Jan 1 2014
Externally publishedYes


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