RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback

Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions. In this paper, we propose RL-VLM-F, a method that automatically generates reward functions for agents to learn new tasks, using only a text description of the task goal and the agent's visual observations, by leveraging feedbacks from vision language foundation models (VLMs). The key to our approach is to query these …

agents challenge cs.ai cs.lg cs.ro design engineering error feedback foundation foundation model functions human iterative language learn paper processes reinforcement reinforcement learning research tasks vision vlm