ML Anthology

Autonomous Helicopter Flight via Reinforcement Learning

H. J. Kim, Michael I. Jordan, Shankar Sastry, Andrew Y. Ng
NeurIPS 2003 pp. 799-806
/neurips/2003/kim2003neurips-autonomous/

Abstract

Autonomous helicopter flight represents a challenging control problem, with complex, noisy, dynamics. In this paper, we describe a successful application of reinforcement learning to autonomous helicopter flight. We first fit a stochastic, nonlinear model of the helicopter dynamics. We then use the model to learn to hover in place, and to fly a number of maneuvers taken from an RC helicopter competition.

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Cite

Text

Kim et al. "Autonomous Helicopter Flight via Reinforcement Learning." Neural Information Processing Systems, 2003.

Markdown

[Kim et al. "Autonomous Helicopter Flight via Reinforcement Learning." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/kim2003neurips-autonomous/)

BibTeX

@inproceedings{kim2003neurips-autonomous,
  title     = {{Autonomous Helicopter Flight via Reinforcement Learning}},
  author    = {Kim, H. J. and Jordan, Michael I. and Sastry, Shankar and Ng, Andrew Y.},
  booktitle = {Neural Information Processing Systems},
  year      = {2003},
  pages     = {799-806},
  url       = {https://mlanthology.org/neurips/2003/kim2003neurips-autonomous/}
}