Papers › FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning

FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning

2 Jun 2024arXiv:2406.00645archive 2025-07-28

Yuwei Fu, Haichao Zhang, Di wu, Wei Xu, Benoit Boulet

In this work, we investigate how to leverage pre-trained visual-language models (VLM) for online Reinforcement Learning (RL). In particular, we focus on sparse reward tasks with pre-defined textual task descriptions. We first identify the problem of reward misalignment when applying VLM as a reward in RL tasks. To address this issue, we introduce a lightweight fine-tuning method, named Fuzzy VLM reward-aided RL (FuRL), based on reward alignment and relay RL. Specifically, we enhance the performance of SAC/DrQ baseline agents on sparse reward tasks by fine-tuning VLM representations and using relay RL to avoid local minima. Extensive experiments on the Meta-world benchmark tasks demonstrate the efficacy of the proposed method. Code is available at: https://github.com/fuyw/FuRL.

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