Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization
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| Title | Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization |
| Authors | |
| Abstract | 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. We further show that using a non-additive criterion (incremental Sharpe Ratio) yields a noisy K-best-paths extraction problem, that can give substantially improved performance. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2007-02-01 |
| Source | Journal of Computers Vol 2, No 1 (2007) |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |