We describe a general method to transform a non-markovian sequential decision problem into a supervised learning problem using a K-best-paths algorithm. We consider an application in financial portfolio management where we can train a controller to directly optimize a Sharpe Ratio (or other risk-averse non-additive) utility function. We illustrate the approach by demonstrating experimental results using a kernel-based controller architecture that would not normally be considered in traditional reinforcement learning or approximate dynamic programming. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Chapados, N., & Bengio, Y. (2006). The K best-paths approach to approximate dynamic programming with application to portfolio optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4013 LNAI, pp. 491–502). Springer Verlag. https://doi.org/10.1007/11766247_42
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