Pearl is a production-ready Reinforcement Learning AI agent library developed by Meta’s Applied Reinforcement Learning team. It enables researchers and practitioners to develop AI agents that learn from cumulative long-term feedback rather than immediate rewards. The library supports environments with limited observability, sparse feedback, and high stochasticity, providing tools to build state-of-the-art RL agents.