C++ Template Library to Predict, Control, Learn Behaviors, and Represent Learnable Knowledge using On/Off Policy Reinforcement Learning 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., < 30 ms). RLLib has been tested and evaluated on RoboCup 3D soccer simulation agents, physical NAO V4 humanoid robots, and Tiva C series launchpad microcontrollers to predict, control, learn behaviors, and represent learnable knowledge. The implementation of the RLLib library is inspired by the RLPark API, which is a library of temporal-difference learning algorithms written in Java.
References in zbMATH (referenced in 1 article )
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- Geramifard, Alborz; Dann, Christoph; Klein, Robert H.; Dabney, William; How, Jonathan P.: RLPy: a value-function-based reinforcement learning framework for education and research (2015)