RLlib

RLlib: Scalable Reinforcement Learning. RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications. RLlib natively supports TensorFlow, TensorFlow Eager, and PyTorch, but most of its internals are framework agnostic.

Keywords for this software

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References in zbMATH (referenced in 6 articles , 1 standard article )

Showing results 1 to 6 of 6.
Sorted by year (citations)

  1. Sébastien M. R. Arnold, Praateek Mahajan, Debajyoti Datta, Ian Bunner, Konstantinos Saitas Zarkias: learn2learn: A Library for Meta-Learning Research (2020) arXiv
  2. Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang, Bowen Xiao, Christina Dan Wang: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance (2020) arXiv
  3. Sergey Kolesnikov, Oleksii Hrinchuk: Catalyst.RL: A Distributed Framework for Reproducible RL Research (2019) arXiv
  4. Yasuhiro Fujita, Toshiki Kataoka, Prabhat Nagarajan, Takahiro Ishikawa: ChainerRL: A Deep Reinforcement Learning Library (2019) arXiv
  5. Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki: RLgraph: Modular Computation Graphs for Deep Reinforcement Learning (2018) arXiv
  6. Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica: RLlib: Abstractions for Distributed Reinforcement Learning (2017) arXiv