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Reinforcement Learning for Bio-Inspired Target Seeking

Gillespie, James, Rano, Ignacio, Siddique, Nazmul, Santos, Jose and Khamassi, Mehdi (2017) Reinforcement Learning for Bio-Inspired Target Seeking. In: Towards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19--21, 2017, Proceedings. Springer International Publishing, pp. 637-650. ISBN 978-3-319-64107-2 [Book section]

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URL: https://doi.org/10.1007/978-3-319-64107-2_52

DOI: 10.1007/978-3-319-64107-2_52

Abstract

Because animals are extremely effective at moving in their natural environments they represent an excellent model to implement robust robotic movement and navigation. Braitenberg vehicles are bio- inspired models of animal navigation widely used in robotics. Tuning the parameters of these vehicles to generate appropriate behaviour can be challenging and time consuming. In this paper we present a Reinforce- ment Learning methodology to learn the sensori-motor connection of Braitenberg vehicle 3a, a biological model of source seeking. We present simulations of different stimuli and reward functions to illustrate the feasibility of this approach.

Item Type:Book section
Keywords:Braitenberg vehicles Reinforcement learning Source seeking
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:38619
Deposited By: Dr Jose Santos
Deposited On:13 Sep 2017 14:06
Last Modified:17 Oct 2017 16:31

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