Autonomous Helicopter Flight via Reinforcement Learning

نویسندگان

  • Andrew Y. Ng
  • H. Jin Kim
  • Michael I. Jordan
  • S. Shankar Sastry
چکیده

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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تاریخ انتشار 2003