Dynamic Programming with NAR Model versus Q-learning — Case Study
Two approaches to control policy synthesis for unknown systems are investigated. An indirect approach is based on adaptive identification of a neural network model in the NAR form (nonlinear autoregresion model) followed by application of the dynamic programming to this model. A direct approach consists of Q-learning with the use of a lookup table. Both methods were applied to optimization of a stock portfolio problem and tested on Warsaw Stock Exchange data.
KeywordsLookup Table Investment Policy Warsaw Stock Exchange Optimal Control Synthesis Cessive Approximation
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