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Mobile robot control by neural networks using self-supervised learning

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5 Author(s)
K. Saga ; Fujitsu Lab. Ltd., Kawasaki, Japan ; T. Sugasaka ; M. Sekiguchi ; S. Nagata
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A reinforcement learning algorithm based on supervised learning is described. It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the system's actions are often inconsistent. In the searching process, the system's actions are always decided stochastically, so the system cannot perform learned actions more than once, even if they have been determined to be suitable actions for the desired task. To solve this problem, a neural network that can predict an evaluation of an action and control the influence of the stochastic element is used. Results from computer simulations using the algorithms to control a mobile robot are described

Published in:

IEEE Transactions on Industrial Electronics  (Volume:39 ,  Issue: 6 )