Pearl: A Production-ready Reinforcement Learning Agent
It provides a production-ready solution for practitioners facing real-world RL deployment challenges, though it appears incremental as it builds on existing RL frameworks.
The paper tackles the challenge of deploying reinforcement learning in production by introducing Pearl, a modular software package that addresses key issues like exploration-exploitation and safety, and demonstrates its industry adoption.
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performant RL policy requires a system designed to address several important challenges, including the exploration-exploitation dilemma, partial observability, dynamic action spaces, and safety concerns. While the importance of these challenges has been well recognized, existing open-source RL libraries do not explicitly address them. This paper introduces Pearl, a Production-Ready RL software package designed to embrace these challenges in a modular way. In addition to presenting benchmarking results, we also highlight examples of Pearl's ongoing industry adoption to demonstrate its advantages for production use cases. Pearl is open sourced on GitHub at github.com/facebookresearch/pearl and its official website is pearlagent.github.io.