Locally Weighted Learning for Control

Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.

[1]  L. A. G. Dresel,et al.  Elementary Numerical Analysis , 1966 .

[2]  R. H. Cannon,et al.  Dynamics of Physical Systems , 1967 .

[3]  James M. Ortega,et al.  Iterative solution of nonlinear equations in several variables , 2014, Computer science and applied mathematics.

[4]  Samuel D. Conte,et al.  Elementary Numerical Analysis: An Algorithmic Approach , 1975 .

[5]  Samuel D. Conte,et al.  Elementary Numerical Analysis , 1980 .

[6]  J. Friedman,et al.  Projection Pursuit Regression , 1981 .

[7]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[8]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[9]  R. Stengel Stochastic Optimal Control: Theory and Application , 1986 .

[10]  Paul E. Utgoff,et al.  Learning to control a dynamic physical system , 1987, Comput. Intell..

[11]  Farmer,et al.  Predicting chaotic time series. , 1987, Physical review letters.

[12]  William H. Press,et al.  Numerical Recipes in FORTRAN - The Art of Scientific Computing, 2nd Edition , 1987 .

[13]  Stephen M. Omohundro,et al.  Efficient Algorithms with Neural Network Behavior , 1987, Complex Syst..

[14]  M. K rn,et al.  Stochastic Optimal Control , 1988 .

[15]  M Kuperstein,et al.  Neural model of adaptive hand-eye coordination for single postures. , 1988, Science.

[16]  J. Doyne Farmer,et al.  Exploiting Chaos to Predict the Future and Reduce Noise , 1989 .

[17]  Christopher G. Atkeson,et al.  Using Local Models to Control Movement , 1989, NIPS.

[18]  A. Barto,et al.  Learning and Sequential Decision Making , 1989 .

[19]  Michael I. Jordan,et al.  Learning to Control an Unstable System with Forward Modeling , 1989, NIPS.

[20]  W. Thomas Miller,et al.  Real-time application of neural networks for sensor-based control of robots with vision , 1989, IEEE Trans. Syst. Man Cybern..

[21]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[22]  F. Frances Yao,et al.  Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[23]  Stephen M. Omohundro,et al.  Bumptrees for Efficient Function, Constraint and Classification Learning , 1990, NIPS.

[24]  Richard S. Sutton,et al.  Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.

[25]  C. Chui,et al.  Approximation Theory VI , 1990 .

[26]  Andrew W. Moore,et al.  Acquisition of Dynamic Control Knowledge for a Robotic Manipulator , 1990, ML.

[27]  A. Moore Variable Resolution Dynamic Programming , 1991, ML.

[28]  Z. Zografski New methods of machine learning for the construction of integrated neuromorphic and associative-memory knowledge bases , 1991, [1991 Proceedings] 6th Mediterranean Electrotechnical Conference.

[29]  Andrew W. Moore,et al.  Fast, Robust Adaptive Control by Learning only Forward Models , 1991, NIPS.

[30]  Andrew W. Moore,et al.  Knowledge of knowledge and intelligent experimentation for learning control , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[31]  David J. C. MacKay,et al.  Bayesian Model Comparison and Backprop Nets , 1991, NIPS.

[32]  Michael I. Jordan,et al.  Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..

[33]  Z. Zografski Geometric and neuromorphic learning for nonlinear modeling, control and forecasting , 1992, Proceedings of the 1992 IEEE International Symposium on Intelligent Control.

[34]  Daniel N. Hill,et al.  An Empirical Investigation of Brute Force to choose Features, Smoothers and Function Approximators , 1992 .

[35]  Sridhar Mahadevan,et al.  Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions , 1992, ML.

[36]  C. Atkeson,et al.  Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time , 1993, Machine Learning.

[37]  Leslie Pack Kaelbling,et al.  Learning in embedded systems , 1993 .

[38]  Andrew W. Moore,et al.  Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation , 1993, NIPS.

[39]  J. Ross Quinlan,et al.  Combining Instance-Based and Model-Based Learning , 1993, ICML.

[40]  Christopher G. Atkeson,et al.  Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming , 1993, NIPS.

[41]  T. Hastie,et al.  Local Regression: Automatic Kernel Carpentry , 1993 .

[42]  Jing Peng,et al.  Efficient Learning and Planning Within the Dyna Framework , 1993, Adapt. Behav..

[43]  J. Peng,et al.  Efficient Learning and Planning Within the Dyna Framework , 1993, IEEE International Conference on Neural Networks.

[44]  Leemon C Baird,et al.  Reinforcement Learning With High-Dimensional, Continuous Actions , 1993 .

[45]  David A. Cohn,et al.  Active Learning with Statistical Models , 1996, NIPS.

[46]  P. van der Smagt,et al.  The locally linear nested network for robot manipulation , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[47]  Dean Pomerleau,et al.  Reliability estimation for neural network based autonomous driving , 1994, Robotics Auton. Syst..

[48]  Thomas H. Connolly,et al.  Comparison of Some Neural Network and Scattered Data Approximations: The Inverse Manipulator Kinematics Example , 1994, Neural Computation.

[49]  S. Schaal,et al.  Robot juggling: implementation of memory-based learning , 1994, IEEE Control Systems.

[50]  Andrew W. Moore,et al.  Efficient Algorithms for Minimizing Cross Validation Error , 1994, ICML.

[51]  David W. Aha,et al.  Learning to Catch: Applying Nearest Neighbor Algorithms to Dynamic Control Tasks , 1994 .

[52]  Andrew W. Moore,et al.  Memory-based Stochastic Optimization , 1995, NIPS.

[53]  Sebastian Thrun,et al.  Is Learning The n-th Thing Any Easier Than Learning The First? , 1995, NIPS.

[54]  Andrew W. Moore,et al.  Multiresolution Instance-Based Learning , 1995, IJCAI.

[55]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[56]  Jing Peng,et al.  Efficient Memory-Based Dynamic Programming , 1995, ICML.

[57]  Andrew G. Barto,et al.  Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..

[58]  Andrew McCallum,et al.  Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State , 1995, ICML.

[59]  Sebastian Thrun,et al.  Discovering Structure in Multiple Learning Tasks: The TC Algorithm , 1996, ICML.

[60]  Prasad Tadepalli,et al.  Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function , 1996, ICML.

[61]  William H. Press,et al.  Numerical recipes in C , 2002 .

[62]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[63]  Andrew W. Moore,et al.  Prioritized sweeping: Reinforcement learning with less data and less time , 2004, Machine Learning.

[64]  Andrew W. Moore,et al.  Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time , 1993, Machine Learning.

[65]  Richard S. Sutton,et al.  Learning to predict by the methods of temporal differences , 1988, Machine Learning.