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JCP
2007

Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization

8 years 9 months ago
Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization
Abstract— We describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-bestpaths 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.
Nicolas Chapados, Yoshua Bengio
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where JCP
Authors Nicolas Chapados, Yoshua Bengio
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