nips nips2003 nips2003-38 nips2003-38-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: H. J. Kim, Michael I. Jordan, Shankar Sastry, Andrew Y. 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.
[1] J. Bagnell and J. Schneider. Autonomous helicopter control using reinforcement learning policy search methods. In Int’l Conf. Robotics and Automation. IEEE, 2001.
[2] G. Balas, J. Doyle, K. Glover, A. Packard, and R. Smith. -analysis and synthesis toolbox user’s guide, 1995.
[3] W. Cleveland. Robust locally weighted regression and smoothing scatterplots. J. Amer. Stat. Assoc, 74, 1979.
[4] Gene F. Franklin, J. David Powell, and Abbas Emani-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 1995.
[5] Y. Ho and X. Cao. Pertubation analysis of discrete event dynamic systems. Kluwer, 1991.
[6] J. Kiefer and J. Wolfowitz. Stochastic estimation of the maximum of a regression function. Annals of Mathematical Statistics, 23:462–466, 1952.
[7] J. Leishman. Principles of Helicopter Aerodynamics. Cambridge Univ. Press, 2000.
[8] A. Y. Ng, D. Harada, and S. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proc. 16th ICML, pages 278–287, 1999.
[9] Andrew Y. Ng. Shaping and policy search in reinforcement learning. PhD thesis, EECS, University of California, Berkeley, 2003.
[10] Andrew Y. Ng and Michael I. Jordan. P EGASUS: A policy search method for large MDPs and POMDPs. In Proc. 16th Conf. Uncertainty in Artificial Intelligence, 2000.
[11] C. Atkeson S. Schaal and A. Moore. Locally weighted learning. AI Review, 11, 1997.
[12] Hyunchul Shim. Hierarchical flight control system synthesis for rotorcraft-based unmanned aerial vehicles. PhD thesis, Mech. Engr., U.C. Berkeley, 2000. S