Asynchronous neurocomputing for optimal control and reinforcement learning with large state spaces

Neurocomputing

Volumn 63, Issue SPEC. ISS., 2005, Pages 229-251

  Scherrer, Bruno ^a,b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b LORIA   (France)

Author keywords

Neurocomputing; Optimal control; Reinforcement learning

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTER ARCHITECTURE; NEURAL NETWORKS; PROBLEM SOLVING; SYNCHRONIZATION;

ASYNCHRONOUS NEUROCOMPUTING; OPTIMAL CONTROL; REINFORCEMENT LEARNING; SPACE CONTROL PROBLEMS;

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; CELL SYNCHRONIZATION; COMPUTER ANALYSIS; MATHEMATICAL COMPUTING; METHODOLOGY; NERVE CELL; PRIORITY JOURNAL; REINFORCEMENT;

EID: 12144262392     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2004.01.192     Document Type: Article

References (18)

1. D.P. Bertsekas J.N. Tsitsiklis Parallel and Distributed Computation Numerical Methods 1989 Prentice-Hall Englewood Cliffs, NJ
2. Y. Burnod An Adaptive Neural Network The Cerebral Cortex 1989 Masson Paris
3. F. Fleuret E. Brunet DEA an architecture for goal planning and classification Neural Comput. 12 9 2000 1987-2008
4. H. Frezza-Buet, Un modèle de cortex pour le comportement motivé d'un agent neuromimétique autonome, Ph.D. Thesis, Université Henri Poincaré, Nancy 1, 1999.
5. J.H. Friedman J.L. Bentley R.A. Finkel An algorithm for finding best matches in logarithmic expected time TOMS 3 3 1977 209-226
6. B. Fritzke, Some Competitive Learning Methods, 1997.
7. V. Gullapalli A.G. Barto Convergence of indirect adaptive asynchronous value iteration algorithms in: D.C. Jack T. Gerald A. Joshua (Eds.) Advances in Neural Information Processing Systems vol. 6 1994 Morgan Kaufmann Publishers Inc. Los Altos, CA 695-702
8. D.O. Hebb The Organization of Behavior A Neuropsychological Theory 1949 Wiley New York
9. L.P. Kaelbling M.L. Littman A.P. Moore Reinforcement learning a survey J. Artif. Intell. Res. 4 1996 237-285
10. M.L. Littman T.L. Dean L.P. Kaelbling On the complexity of solving Markov decision problems in: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-95) 1995 Montreal, Québec Canada 394-402
11. W.S. McCulloch, W.P. Pitts, A logical calculus in the ideas immanent in nervous activity, Bull. Math. Biophys., 1943.
12. R. Munos, A. Moore, Rates of convergence for variable resolution schemes in optimal control, in: International Conference on Machine Learning, 2000.
13. M. Puterman Markov Decision Processes 1994 Wiley New York
14. N. Rougier, Modèles de mémoire pour la navigation autonome, Ph.D. Thesis, Université Henri Poincaré, Nancy 1, 2000.
15. B. Scherrer, Apprentissage de représentation et auto-organisation modulaire pour un agent autonome, Ph.D. Thesis, Université Henri Poincaré, Nancy 1, January 2003.
16. B. Scherrer Parallel asynchronous distributed computations of optimal control in large state space markov decision processes in: European Symposium on Artificial Neural Networks April 2003 Bruges Belgique
17. A. Scheuer, Planification de chemins à courbure continue, Ph.D. Thesis, Institut National Polytechnique de Grenoble, 1998.
18. R.S. Sutton A.G. Barto Reinforcement learning, An introduction, BradFord Book 1998 The MIT Press Cambridge, MA