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., ≤30ms). 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.
CITATION STYLE
Abeyruwan, S., & Visser, U. (2015). Rllib: C++ library to predict, control, and represent learnable knowledge using on/off policy reinforcement learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9513, pp. 356–364). Springer Verlag. https://doi.org/10.1007/978-3-319-29339-4_30
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