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Source note3 min read

Automating Potential-based Reward Shaping with Vision Language Model Guidance

Source

arXiv

arxiv.org/abs/2606.27180v1

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Source

Plain-Language Brief

Researchers have developed a new method called VLM-PBRS for automating potential-based reward shaping in reinforcement learning agents. This method uses vision language models (VLMs) to learn the potential function directly from feedback, which helps the agent explore more effectively and avoid exploiting auxiliary signals. The authors claim that their approach preserves original optimal policies and improves sample efficiency while being robust against reward hacking.

What the Document Says

  • The text discusses a new method called VLM-PBRS for potential-based reward shaping in reinforcement learning agents.
  • VLM-PBRS learns the potential function from vision language model feedback.
  • The potential function guides exploration and helps attribute success rewards to relevant parts of the trajectory.
  • Naive reward shaping can induce reward hacking, but VLM-PBRS preserves original optimal policies without requiring expert-designed reward shaping terms.
  • VLMs are prohibitively expensive to invoke repeatedly during policy learning, so smaller, more computationally efficient VLMs are used instead for obtaining preference labels.
  • Empirical evidence shows that even less accurate preference labels can still accelerate learning.
  • The method has been validated empirically in the Meta-World and Franka Kitchen environments.

Key Concepts Mentioned

  • reinforcement learning
  • potential-based reward shaping
  • vision language models
  • sample efficiency
  • reward hacking

Claims Made by the Document

  • VLM-PBRS preserves original optimal policies.
  • VLM-PBRS removes the need for expert-designed reward shaping terms.
  • Smaller VLMs can still be used to obtain preference labels for accelerating learning, even if they are less accurate.

Evidence Used

  • The text states that because VLM-PBRS is based on potential-based reward shaping, it preserves the original optimal policies.
  • The authors claim that their approach removes the need for expert-designed reward shaping terms.
  • The empirical evidence is mentioned but not detailed in the text.

Methods / Approach

  • VLM-guided PBRS framework
  • Querying a lightweight VLM to obtain preferences over image pairs
  • Training a model of the potential function using preference labels

Limitations Stated by the Authors

  • Large VLMs are prohibitively expensive to invoke repeatedly during policy learning.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The text lacks information about other limitations or challenges associated with the method.

Open Questions

  • What specific environments were used for empirical validation?
  • What is the accuracy of the preference labels obtained from smaller VLMs compared to larger ones?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The text does not explicitly state the specific environments used for empirical validation, but it mentions Meta-World and Franka Kitchen.

Raw Abstract / Excerpt

Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly attribute the sparse success rewards to relevant parts of the trajectory. Naive reward shaping can induce reward hacking, yielding policies that exploit auxiliary signals instead of solving the intended task. Potential-based reward shaping (PBRS) guarantees preservation of the optimal policy set, but requires the definition of a heuristic potential function over the state space. In this work, we introduce the VLM-guided PBRS framework VLM-PBRS that learns the potential function directly from vision language model (VLM) feedback. We query a lightweight VLM to obtain preferences over image pairs and train a model of the potential function using these preferences. As this approach is based on potential-based reward shaping, it preserves the original optimal policies, and removes the need for expert-designed reward shaping terms. Because large VLMs are prohibitively expensive to invoke repeatedly during policy learning, we employ smaller, more computationally efficient VLMs. Although the resulting preference labels are less accurate, empirical evidence shows that the preference labels can still be used to accelerate learning. We validate our method empirically in the Meta-World and Franka Kitchen environments and highlight the connection between VLM preference label accuracy and sample efficiency improvements. Our contributions are threefold: (1) the first application of VLM preference-based learning to synthesize a potential function for PBRS, (2) a principled, low-cost solution that leverages small VLMs, and (3) extensive empirical demonstration of improved sample efficiency and robustness to reward hacking.