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Deep Reinforcement Learning System

For now, this category only contains system for drl papers and projects.

  • Mao, Hongzi, et al. "Park: An Open Platform for Learning-Augmented Computer Systems." Advances in Neural Information Processing Systems. 2019.
  • Summary: This work builds a platform to introduce DRL to computer system optimizaton. It provides a lot of APIs so researcher can focus on developing algorithm rather spend a lot of time on writing system engineering codes.
  • Ray: A Distributed Framework for Emerging {AI} Applications [GitHub]
    • Moritz, Philipp, et al. (OSDI 2018)
    • Summary: Distributed DRL training, simulation and inference system. Can be used as a high-performance python framework.
  • Elf: An extensive, lightweight and flexible research platform for real-time strategy games [Paper] [GitHub]
    • Tian, Yuandong, Qucheng Gong, Wenling Shang, Yuxin Wu, and C. Lawrence Zitnick. (NIPS 2017)
    • Summary:
  • Horizon: Facebook's Open Source Applied Reinforcement Learning Platform [Paper] [GitHub]
    • Gauci, Jason, et al. (preprint 2019)
  • RLgraph: Modular Computation Graphs for Deep Reinforcement Learning [Paper][GitHub]
    • Schaarschmidt, Michael, Sven Mika, Kai Fricke, and Eiko Yoneki. (SysML 2019)
    • Summary: