  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.
