A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics
Hervé Frezza-Buet, Matthieu Geist; 14(18):625−628, 2013.
Abstract
This paper introduces the rllib as an original C++ template-based library oriented toward value function estimation. Generic programming is promoted here as a way of having a good fit between the mathematics of reinforcement learning and their implementation in a library. The main concepts of rllib are presented, as well as a short example.
[abs]
[pdf][bib] [code]| © JMLR 2013. (edit, beta) |