AgenticRL deploys a multimodal GPT agent in a closed-loop process to autonomously design and refine reward functions for PPO-trained vision-conditioned UAV navigation policies, reporting 71% policy improvement and 91% real-world success.
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AgenticRL: Self-Refining Agentic Reinforcement Learning for Vision-Conditioned UAV Navigation
AgenticRL deploys a multimodal GPT agent in a closed-loop process to autonomously design and refine reward functions for PPO-trained vision-conditioned UAV navigation policies, reporting 71% policy improvement and 91% real-world success.