TY - GEN
T1 - Reinforcement-driven shaping of sequence learning in neural dynamics
AU - Luciw, Matthew
AU - Kazerounian, Sohrob
AU - Sandamirskaya, Yulia
AU - Schöner, Gregor
AU - Schmidhuber, Jürgen
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - http://link.springer.com/10.1007/978-3-319-08864-8_19
UR - http://www.scopus.com/inward/record.url?scp=84958535239&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08864-8_19
DO - 10.1007/978-3-319-08864-8_19
M3 - Conference contribution
SN - 9783319088631
SP - 198
EP - 209
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer [email protected]
ER -