Reward-driven Training of Random Boolean Network Reservoirs for Model-Free Environments

Reward-driven Training of Random Boolean Network Reservoirs for Model-Free Environments

Reservoir Computing (RC) is an emerging machine learning paradigm where a fixed kernel, built from a randomly connected "reservoir" with sufficiently rich dynamics, is capable of expanding the problem space in a non-linear fashion to a higher dimensional feature space. These features can t...

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Bibliographic Details
Main Author: Gargesa, Padmashri
Format: Others
Published: PDXScholar 2013
Subjects:
Computer networks > Technological innovations
Context-aware computing
Machine learning
Dynamic programming
Evolutionary computation
Dynamics and Dynamical Systems
Electrical and Computer Engineering
Online Access:https://pdxscholar.library.pdx.edu/open_access_etds/669
https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1668&context=open_access_etds

Internet

https://pdxscholar.library.pdx.edu/open_access_etds/669
https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1668&context=open_access_etds

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