Summary of Furl: Visual-language Models As Fuzzy Rewards For Reinforcement Learning, by Yuwei Fu et al.
FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning
by Yuwei Fu, Haichao Zhang, Di Wu, Wei Xu, Benoit Boulet
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers explore how to utilize pre-trained visual-language models (VLMs) in online Reinforcement Learning (RL) settings, specifically focusing on sparse reward tasks with predefined textual descriptions. They identify the challenge of reward misalignment when using VLMs as rewards and introduce a lightweight fine-tuning method called Fuzzy VLM reward-aided RL (FuRL), which combines reward alignment and relay RL to improve performance. The proposed approach is evaluated on the Meta-world benchmark tasks, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special AI models that can understand both images and text to help online learning systems make better decisions. These models are pre-trained to recognize patterns in language and vision, but they need to be fine-tuned for specific tasks. The researchers created a new way to do this called FuRL, which helps the model learn from rewards even when they’re hard to come by. They tested FuRL on some challenging tasks and found that it worked really well. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Online learning » Reinforcement learning
