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
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 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||