RL^2 capability: fast reinforcement learning via slow reinforcement learning

AI Impact Summary

RL^2 introduces meta-learning to reinforcement learning, enabling a fast learner to adapt to new tasks using a slow, overarching training process. This approach can cut the data and interaction requirements for policy adaptation, reducing time-to-performance for RL workloads in production. Teams deploying RL-based control, recommendation, or optimization services will need to adjust their training pipelines to enable a slow-learned meta-learner and a fast-adaptation policy, with attention to data management and evaluation strategies.