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

• URL: http://arxiv.org/abs/2406.00645v2
• Date: Wed, 5 Jun 2024 00:05:23 GMT
• Title: FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning
• Authors: Yuwei Fu, Haichao Zhang, Di Wu, Wei Xu, Benoit Boulet,
• Abstract summary: We investigate how to leverage pre-trained visual-language models (VLM) for online Reinforcement Learning (RL) We first identify the problem of reward misalignment when applying VLM as a reward in RL tasks. We introduce a lightweight fine-tuning method, named Fuzzy VLM reward-aided RL (FuRL)
• Score: 18.60627708199452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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