title: Pearl: A Production-ready Reinforcement Learning Agent
summary: Reinforcement Learning (RL) offers a versatile framework for achieving
  long-term goals. Its generality allows us to formalize a wide range of problems
  that real-world intelligent systems encounter, such as dealing with delayed
  rewards, handling partial observability, addressing the exploration and
  exploitation dilemma, utilizing offline data to improve online performance, and
  ensuring safety constraints are met. Despite considerable progress made by the
  RL research community in addressing these issues, existing open-source RL
  libraries tend to focus on a narrow portion of the RL solution pipeline,
  leaving other aspects largely unattended. This paper introduces Pearl, a
  Production-ready RL agent software package explicitly designed to embrace these
  challenges in a modular fashion. In addition to presenting preliminary
  benchmark results, this paper highlights Pearl's industry adoptions to
  demonstrate its readiness for production usage. Pearl is open sourced on Github
  at github.com/facebookresearch/pearl and its official website is located at
  pearlagent.github.io.
paper_authors._id: 65729188db258f2566881561
paper_authors.name: Zheqing Zhu
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paper_authors.fullname: Zheqing Zhu
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paper_authors.user: billmatrix
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paper_authors.name: Rodrigo de Salvo Braz
paper_authors._id: 65729188db258f2566881563
paper_authors.name: Jalaj Bhandari
paper_authors._id: 65729188db258f2566881564
paper_authors.name: Daniel Jiang
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paper_authors.fullname: Daniel Jiang
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paper_authors.user: danielrjiang
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paper_authors.name: Yi Wan
paper_authors._id: 65729188db258f2566881566
paper_authors.name: Yonathan Efroni
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paper_authors.name: Liyuan Wang
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paper_authors.fullname: Liyuan Wang
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paper_authors.name: Ruiyang Xu
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paper_authors.name: Hongbo Guo
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paper_authors.fullname: guohongbo
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paper_authors.user: guohongbo
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paper_authors.name: Alex Nikulkov
paper_authors._id: 65729188db258f256688156b
paper_authors.name: Dmytro Korenkevych
paper_authors._id: 65729188db258f256688156c
paper_authors.name: Urun Dogan
paper_authors._id: 65729188db258f256688156d
paper_authors.name: Frank Cheng
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paper_authors.fullname: Frank Cheng
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paper_authors.user: frankcheng
paper_authors._id: 65729188db258f256688156e
paper_authors.name: Zheng Wu
paper_authors._id: 65729188db258f256688156f
paper_authors.name: Wanqiao Xu
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/0c3e5538ad7095484e3554a0959dba77.svg
paper_authors.fullname: Wanqiao Xu
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paper_title: Pearl: A Production-ready Reinforcement Learning Agent
paper_summary: Reinforcement Learning (RL) offers a versatile framework for achieving
  long-term goals. Its generality allows us to formalize a wide range of problems
  that real-world intelligent systems encounter, such as dealing with delayed
  rewards, handling partial observability, addressing the exploration and
  exploitation dilemma, utilizing offline data to improve online performance, and
  ensuring safety constraints are met. Despite considerable progress made by the
  RL research community in addressing these issues, existing open-source RL
  libraries tend to focus on a narrow portion of the RL solution pipeline,
  leaving other aspects largely unattended. This paper introduces Pearl, a
  Production-ready RL agent software package explicitly designed to embrace these
  challenges in a modular fashion. In addition to presenting preliminary
  benchmark results, this paper highlights Pearl's industry adoptions to
  demonstrate its readiness for production usage. Pearl is open sourced on Github
  at github.com/facebookresearch/pearl and its official website is located at
  pearlagent.github.io.
paper_ai_summary: Pearl is a modular, production-ready RL agent software package addressing various challenges in reinforcement learning.
paper_ai_keywords: Reinforcement Learning (RL) delayed rewards partial observability exploration and exploitation offline data safety constraints modular fashion