Pearl: A Production-ready Reinforcement Learning Agent
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.
65729188db258f2566881561
Zheqing Zhu
admin_assigned
2023-12-08T10:46:05.551Z
64ea85fa2f70f2a4c75e5bff
/avatars/48841e49d0e1d9a907019c31454245e6.svg
Zheqing Zhu
user
billmatrix
65729188db258f2566881562
Rodrigo de Salvo Braz
65729188db258f2566881563
Jalaj Bhandari
65729188db258f2566881564
Daniel Jiang
admin_assigned
2023-12-08T10:46:41.804Z
65085762eac45ee2e43e2242
/avatars/48f163126da18979d148f6b06a3aecaa.svg
Daniel Jiang
user
danielrjiang
65729188db258f2566881565
Yi Wan
65729188db258f2566881566
Yonathan Efroni
65729188db258f2566881567
Liyuan Wang
admin_assigned
2023-12-08T10:47:10.219Z
63ffd0b1cafc9d54986048dc
/avatars/92e8fa331e3365172f75ef77ebed5577.svg
Liyuan Wang
user
summeee
65729188db258f2566881568
Ruiyang Xu
65729188db258f2566881569
Hongbo Guo
admin_assigned
2023-12-08T10:47:52.014Z
64748e27a855203d8fea8abe
/avatars/fe24291647bfd504a9158f900c1bff7a.svg
guohongbo
user
guohongbo
65729188db258f256688156a
Alex Nikulkov
65729188db258f256688156b
Dmytro Korenkevych
65729188db258f256688156c
Urun Dogan
65729188db258f256688156d
Frank Cheng
admin_assigned
2023-12-08T10:48:09.667Z
644230d40f373bdb046381e0
/avatars/709b3930039ef00216086be99698250f.svg
Frank Cheng
user
frankcheng
65729188db258f256688156e
Zheng Wu
65729188db258f256688156f
Wanqiao Xu
admin_assigned
2023-12-08T10:48:49.796Z
63918510239695d2240a7918
/avatars/0c3e5538ad7095484e3554a0959dba77.svg
Wanqiao Xu
user
wanqiaox
Pearl: A Production-ready Reinforcement Learning Agent
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.
Pearl is a modular, production-ready RL agent software package addressing various challenges in reinforcement learning.
Reinforcement Learning (RL) delayed rewards partial observability exploration and exploitation offline data safety constraints modular fashion