Reinforcement learning and CMAC-based adaptive routing for MANETs

Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICON.2004.1409226

Title:	Reinforcement learning and CMAC-based adaptive routing for MANETs
Authors:	Chetret, D. Tham, C.-K. Wong, L.W.C.
Issue Date:	2004
Citation:	Chetret, D.,Tham, C.-K.,Wong, L.W.C. (2004). Reinforcement learning and CMAC-based adaptive routing for MANETs. Proceedings - IEEE International Conference on Networks, ICON 2 : 540-544. ScholarBank@NUS Repository. https://doi.org/10.1109/ICON.2004.1409226
Abstract:	A novel routing scheme, which combines the on-demand routing capability of Ad Hoc On-Demand Vector (AODV) routing protocol with a Q-routing inspired route selection mechanism, is proposed in this paper. The scheme makes routing choices based on local information (such as mobility, power remaining at the neighbouring nodes) and past experience. The CMAC (Cerebellar Model Articulation Controller) function approximator is used to accelerate the reinforcement learning. Through extensive simulation, we demonstrate that our scheme is effective in improving end-to-end delay, without requiring much of the limited network resources. © 2004 IEEE.
Source Title:	Proceedings - IEEE International Conference on Networks, ICON
URI:	http://scholarbank.nus.edu.sg/handle/10635/71599
ISBN:	078038783X
ISSN:	15312216
DOI:	10.1109/ICON.2004.1409226
Appears in Collections:	Staff Publications

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