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Neural plasticity and minimal topologies for reward-based learning

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conference contribution
posted on 18.03.2015, 13:47 by Andrea SoltoggioAndrea Soltoggio
Artificial Neural Networks for online learning problems are often implemented with synaptic plasticity to achieve adaptive behaviour. A common problem is that the overall learning dynamics are emergent properties strongly dependent on the correct combination of neural architectures, plasticity rules and environmental features. Which complexity in architectures and learning rules is required to match specific control and learning problems is not clear. Here a set of homosynaptic plasticity rules is applied to topologically unconstrained neural controllers while operating and evolving in dynamic reward-based scenarios. Performances are monitored on simulations of bee foraging problems and T-maze navigation. Varying reward locations compel the neural controllers to adapt their foraging strategies over time, fostering online reward-based learning. In contrast to previous studies, the results here indicate that reward-based learning in complex dynamic scenarios can be achieved with basic plasticity rules and minimal topologies. © 2008 IEEE.

History

School

  • Science

Department

  • Computer Science

Published in

Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008

Pages

637 - 642

Citation

SOLTOGGIO, A., 2008. Neural plasticity and minimal topologies for reward-based learning. IN: Proceedings of the 8th International Conference on Hybrid Intelligent Systems, HIS 2008, pp. 637 - 642.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2008

Notes

This is a conference paper © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9780769533261

Language

en

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