Instead of manually designing reward functions for robot learning, use an AI agent to generate, evaluate, and refine rewards automatically—this reduces human effort and improves policy performance by 71% through closed-loop self-improvement.
AgenticRL is a framework that uses a multimodal AI agent to automatically design reward functions, train drone navigation policies, and refine them through feedback loops—eliminating manual reward engineering.