Team tao

Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry
Other Grants and Activities

Section: Software

Keywords : Stochastic Dynamic Programming, Learning, Object-oriented.


Participants : Olivier Teytaud [ correspondent ] , Sylvain Gelly, Jérémie Mary.

Abstract: OpenDP is a young open source code for stochastic dynamic programming, based upon the use of (i) time-decomposition as in standard dynamic programming (ii) learning (iii) derivative-free optimization. It is designed in a very modular manner, including many existing source codes: OpenBeagle (with the help of Christian Gagné), EO (with the help of Damien Tessier), CoinDFO, Opt++, and many others, for optimization; the Weka algorithms and some others for learning. It also includes various benchmarks.

The inclusion of tools from various areas of science is under work, such as time-pca, robotic-mapping, derandomization of random processes. If many of these tools are not new, their use in the framework of dynamic programming is new. The software is already parallel and has provided many results, among which a comparison of function-values approximators (no so large comparison existed in the literature, many published papers only considering one learning method, not necessarily in the same conditions than other published results) and derivative-free optimization algorithms in the case of a very restricted number of iterates. The use of benchmarks that are not in the traditional goals of stochastic dynamic programming is also new.

As a side effect of this software development:

Contacts have been developped with industrial and applied users of dynamic programming, such as Artelys, Cemagref and EdF.

See main page at .


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