Learning Autonomous Helicopter Flight with Evolutionary Reinforcement Learning

  • José Antonio Martín H.
  • Javier de Lope
Conference paper

DOI: 10.1007/978-3-642-04772-5_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5717)
Cite this paper as:
Martín H. J.A., de Lope J. (2009) Learning Autonomous Helicopter Flight with Evolutionary Reinforcement Learning. In: Moreno-Díaz R., Pichler F., Quesada-Arencibia A. (eds) Computer Aided Systems Theory - EUROCAST 2009. EUROCAST 2009. Lecture Notes in Computer Science, vol 5717. Springer, Berlin, Heidelberg

Abstract

In this paper we present a method to obtain a near optimal neuro-controller for the autonomous helicopter flight by means of an ad hoc evolutionary reinforcement learning method. The method presented here was developed for the Second Annual Reinforcement Learning Competition (RL2008) held in Helsinki-Finland. The present work uses a Helicopter Hovering simulator created in the Stanford University that simulates a Radio Control XCell Tempest helicopter in the flight regime close to hover. The objective of the controller is to hover the helicopter by manipulating four continuous control actions based on a 12-dimensional state space.

Keywords

Reinforcement Learning Evolutionary Computation Autonomous Helicopter 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • José Antonio Martín H.
    • 1
  • Javier de Lope
    • 2
  1. 1.Dep. Sistemas Informáticos y ComputaciónUniversidad Complutense de MadridSpain
  2. 2.Dept. Applied Intelligent SystemsUniversidad Politécnica de MadridSpain

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