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


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.

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