Reinforcement Learning for Bio-Inspired Target Seeking

James Gillespie, Ignacio Rano, Nazmul Siddique, Jose Santos, Mehdi Khamassi

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19--21, 2017, Proceedings
PublisherSpringer
Pages637-650
ISBN (Print)978-3-319-64107-2
DOIs
Publication statusE-pub ahead of print - 20 Jul 2017

Bibliographical note

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Keywords

  • Braitenberg vehicles
  • Reinforcement learning
  • Source seeking

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