@inproceedings{a67f4fde1c964ccbb55d9a999e76d728,
title = "Reinforcement Learning for Bio-Inspired Target Seeking",
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 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.",
keywords = "Braitenberg vehicles, Reinforcement learning, Source seeking",
author = "James Gillespie and I{\~n}aki Ra{\~n}{\'o} and Nazmul Siddique and Jos{\'e} Santos and Mehdi Khamassi",
year = "2017",
doi = "10.1007/978-3-319-64107-2_52",
language = "English",
isbn = "978-3-319-64106-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "637--650",
editor = "Yang Gao and Saber Fallah and Yaochu Jin and Constantina Lekakou",
booktitle = "Towards Autonomous Robotic Systems",
address = "Germany",
}