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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3
Dynamic Programming with ARMA, Markov, and NARMA Models vs. Q-Learning: Case Study
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
Jaroslaw Chrobak, Warsaw University of Technology
Andrzej Pacut, Warsaw University of Technology
Andrzej Karbowski, Warsaw University of Technology
Two approaches to control policy synthesis for unknown systems are investigated. The indirect approach is based on the identification of ARMA, NARMA, or Markov chain models, and application of dynamic programming to these models with or without use of the certainty equivalence principle. The direct approach is represented here by Q -learning, with the lookup table or with the use of radial basis function approximation. We implemented both methods to optimization of a stock portfolio and tested on Warsaw Stock Exchange data.
Citation:
Jaroslaw Chrobak, Andrzej Pacut, Andrzej Karbowski, "Dynamic Programming with ARMA, Markov, and NARMA Models vs. Q-Learning: Case Study," ijcnn, vol. 3, pp.3265, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000
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