Autonomous Helicopter Flight via Reinforcement Learning

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

Authors

H. Kim, Michael Jordan, Shankar Sastry, Andrew Ng

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.