Reinforcement Learning for Bio-Inspired Target Seeking

James Gillespie, Iñaki Rañó, Nazmul Siddique, José Santos, Mehdi Khamassi

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Abstrakt

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 Reinforcement 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.
OriginalsprogEngelsk
TitelTowards Autonomous Robotic Systems : 18th Annual Conference
RedaktørerYang Gao, Saber Fallah, Yaochu Jin, Constantina Lekakou
ForlagSpringer
Publikationsdato2017
Sider637-650
ISBN (Trykt)978-3-319-64106-5
ISBN (Elektronisk)978-3-319-64107-2
DOI
StatusUdgivet - 2017
Udgivet eksterntJa
NavnLecture Notes in Computer Science
Vol/bind10454
ISSN0302-9743

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