A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics
Hervé Frezza-Buet, Matthieu Geist.
Year: 2013, Volume: 14, Issue: 18, Pages: 625−628
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.