@inbook{d06539c2d7ca4203ad14dc435c0a4792,
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 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.",
keywords = "Braitenberg vehicles, Reinforcement learning, Source seeking",
author = "James Gillespie and Ignacio Rano and Nazmul Siddique and Jose Santos and Mehdi Khamassi",
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year = "2017",
month = jul,
day = "20",
doi = "10.1007/978-3-319-64107-2_52",
language = "English",
isbn = "978-3-319-64107-2",
pages = "637--650",
booktitle = "Towards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19--21, 2017, Proceedings",
publisher = "Springer",
address = "Switzerland",
}