Abstract
RLLib is a lightweight C++ template library that implements incremental, standard, and gradient temporal-difference learning algorithms in reinforcement learning. It is an optimized library for robotic applications and embedded devices that operates under fast duty cycles (e.g., \documentclass[12pt]{minimal}
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\begin{document}$$\le $$\end{document}30 ms). RLLib has been tested and evaluated on RoboCup 3D soccer simulation agents, NAO V4 humanoid robots, and Tiva C series launchpad microcontrollers to predict, control, learn behavior, and represent learnable knowledge.
