MetaCartSign in to MyCiteSeer

Include Citations | Advanced Search | Help

Include Citations | Advanced Search | Help

  Learning to control dynamic systems via associative

Download:
Download as a PDF | Download as a PS
by Vijaykumar Gullapalli
ftp://ftp.cs.umass.edu/pub/anw/pub/vijay/wileych.ps.gz
Add To MetaCart

Abstract:

reinforcement learning

Citations

4828 Genetic Algorithms – Goldberg - 1989
2961 Pattern Classification and Scene Analysis – Duda, Hart - 1973
2140 Learning Internal Representations by Error Propagation – Rumelhart, Hinton, et al. - 1986
938 Learning from Delayed Rewards – Watkins - 1989
885 Learning to Predict by the Methods of Temporal Differences – Sutton - 1988
366 Beyond Regression: New Tools for Prediction and Analysis – Werbos - 1974
295 A learning algorithm for Boltzmann Machines – Ackley, Hinton, et al. - 1985
273 Connectionist learning procedures – Hinton - 1989
252 A stochastic approximation method – Robbins, Munro - 1951
212 Temporal Credit Assignment in Reinforcement Learning – Sutton - 1984
196 Forward Models: Supervised Learning with a Distal Teacher – Jordan, Rumelhart - 1992
177 Distributed representations – Hinton, McClelland, et al. - 1986
171 Unsupervised learning – Barlow - 1989
135 Neuronlike elements that can solve difficult learning control problems – Barto, Sutton, et al. - 1983
113 Learning with localized receptive fields – Moody, Darken - 1988
112 BOXES: An experiment in adaptive control – Michie, Chambers - 1968
105 Learning Automata --- an Introduction – Narendra, Thathachar - 1989
91 A hierarchical neural-network model for control and learning of voluntary movement – Kawato, Furukawa, et al. - 1987
91 Efficient Memory-based Learning for Robot Control – Moore - 1990
87 The truck backer-upper: An example of selflearning in neural networks – Nguyen, Widrow - 1990
85 Quasi-static assembly of compliantly supported rigid parts – Whitney - 1982
68 Learning to control an inverted pendulum using neural networks – Anderson - 1989
63 Learning to control an unstable system with forward modeling – Jordan, Jacobs - 1990
59 Stochastic estimation of the maximum of a regression function – Kiefer, Wolfowitz - 1952
55 Learning logic – Parker - 1985
53 A stochastic reinforcement learning algorithm for learning realvalued functions. Neural Networks 3 – Gullapalli - 1990
52 Pattern–recognizing stochastic learning automata’, in – Barto, Anandan - 1985
49 An application of the principle of maximum information preservation to linear systems – Linsker - 1989
47 Connectionist learning for control: An overview – Barto - 1990
43 Training and tracking in robotics – Selfridge, Sutton, et al. - 1985
41 Weight Perturbation: An Optimal Architecture and learning Technique for Analog VLSI Feedforward and recurrent Multilayer Networks – Jabri, Flower - 1992
41 Feedback Control Systems – Smith - 1958
39 A menu of designs for reinforcement learning over time – Werbos - 1990
38 Supervised learning and systems with excess degrees of freedom – Jordan - 1988
37 Une procédure d’apprentissage pour réseau à seuil assymétrique. In Cognitiva 85: A la Frontière de l’Intelligence Artificielle des Sciences de la Connaissance des Neurosciences – LeCun - 1985
33 Computational schemes and neural network models for formation and control of multijoint arm trajectory – Kawato - 1990
32 Associative search networks: a reinforcement learning associativememory – Barto, Sutton, et al. - 1981
32 A dual back-propagation scheme for scalar reward learning – Munro - 1987
32 Evolving Controls for Unstable Systems – Wieland - 1990
29 Automaton Theory and Modeling of Biological Systems – Tsetlin - 1973
26 Gradient following without back-propagation in layered networks – Barto, Jordan - 1987
24 Using associative content-addressable memories to control robots – Atkeson, Reinkensmeyer - 1989
24 A self-learning automaton with variable resolution for high precision assembly by industrial robots – Simons, Brussel, et al. - 1982
22 Learning to control a dynamic physical system – Connell, Utgoff - 1987
22 On the use of backpropagation in associative reinforcement learning – Williams - 1988
20 Reinforcement learning in connectionist networks: A mathematical analysis – Williams - 1986
18 Reinforcement-Learning Connectionist Systems – Williams - 1987
17 Action – Jordan, Rosenbaum - 1988
17 Hierarchical Neural Network Model for Voluntary Movement with Application to Robotics – Kawato, Uno, et al. - 1988
16 Neural networks and radial basis functions in classifying static speech patterns – Niranjan, Fallside - 1990