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A fast learning variable lambda TD model: Used to realize home aware robot navigation
2014
2014 International Joint Conference on Neural Networks (IJCNN)
This work describes a fast learning robot goalaware navigation model that employs both gradient and conjugate gradient Temporal Difference (TD, TD-conj) methods. It builds on the fact that TD-conj was proven to be equivalent to a gradient TD method with a variable lambda under certain conditions. Based on straightforward features extraction process combined with goal-aware capabilities provided by whole image measure, the model solves what we call u-turn-homing benchmark problem without using
doi:10.1109/ijcnn.2014.6889845
dblp:conf/ijcnn/Altahhan14
fatcat:nxikdv3wajagzgow7ch5rhgzzi