Pearl offers a range of unique features for production environments, including dynamic action spaces, offline learning, intelligent neural exploration, safe decision making, history summarization, and data augmentation. The library is currently being used to support real-world applications such as recommender systems, auction bidding systems, and creative selection. A more detailed tutorial on Pearl will be presented at the NeurIPS 2023 EXPO presentation.
Key takeaways:
- Pearl is a new production-ready Reinforcement Learning AI agent library open-sourced by the Applied Reinforcement Learning team at Meta.
- The library enables researchers and practitioners to develop Reinforcement Learning AI agents that can adapt to environments with limited observability, sparse feedback, and high stochasticity.
- Pearl offers a diverse set of unique features for production environments, including dynamic action spaces, offline learning, intelligent neural exploration, safe decision making, history summarization, and data augmentation.
- Pearl is currently supporting real-world applications such as recommender systems, auction bidding systems, and creative selection.