Rllib: C++ library to predict, control, and represent learnable knowledge using on/off policy reinforcement learning

Saminda Abeyruwan, Ubbo E Visser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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., ≤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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages356-364
Number of pages9
Volume9513
ISBN (Print)9783319293387
DOIs
StatePublished - 2015
Event19th Annual RoboCup International Symposium, 2015 - Hefei, China
Duration: Jul 23 2015Jul 23 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9513
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other19th Annual RoboCup International Symposium, 2015
CountryChina
CityHefei
Period7/23/157/23/15

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Keywords

  • Gradient temporal-difference
  • Reinforcement learning
  • RLLib

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Abeyruwan, S., & Visser, U. E. (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). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9513). Springer Verlag. https://doi.org/10.1007/978-3-319-29339-4_30