Repository | Book | Chapter
(2015) Artificial life and computational intelligence, Dordrecht, Springer.
Learning nursery rhymes using adaptive parameter neurodynamic programming
Josiah Walker , Stephan K. Chalup
pp. 196-209
In this study on music learning, we develop an average reward based adaptive parameterisation for reinforcement learning meta-parameters. These are tested using an approximation of user feedback based on the goal of learning the nursery rhymes Twinkle Twinkle Little Star and Mary Had a Little Lamb. We show that a large reduction in learning times can be achieved through a combination of adaptive parameters and random restarts to ensure policy convergence.
Publication details
DOI: 10.1007/978-3-319-14803-8_16
Full citation:
Walker, J. , Chalup, S. K. (2015)., Learning nursery rhymes using adaptive parameter neurodynamic programming, in M. Randall (ed.), Artificial life and computational intelligence, Dordrecht, Springer, pp. 196-209.