Description: 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 active training introduces Pearl, a Production-ready RL agent software package explicitly designed to embrace these challenges in a modular fashion and we will teach our attendees how to leverage this package to address some real-world complex problems that require multiple capabilities from a RL agent.
Learning objectives: - Hands-on tutorial on Meta’s open-source reinforcement learning software package - Deep dive on real-world reinforcement learning applications and how different RL capabilities are involved - Discussion on future directions of reinforcement learning applications in the real-world. - Understanding reinforcement learning concepts and how that translates to industry applications.