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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 introduce a new framework called VLM-PBRS for automating potential-based reward shaping in reinforcement learning agents. The framework learns the potential function directly from vision language model (VLM) feedback, which helps in guiding exploration and solving tasks without expert-designed reward shaping terms. The method uses smaller, more computationally efficient VLMs due to the cost of invoking large ones repeatedly during policy learning.

What the Document Says

  • The paper presents a new framework called VLM-PBRS that learns potential functions for potential-based reward shaping (PBRS) from vision language model (VLM) feedback.
  • The method involves querying a lightweight VLM to obtain preferences over image pairs, and training a potential function model using these preferences.
  • Since the approach is based on PBRS, it preserves the original optimal policies.
  • To reduce costs, smaller, more computationally efficient VLMs are used instead of large ones.
  • Empirical evidence shows that the preference labels can still be used to accelerate learning even though they may not be as accurate.

Key Concepts Mentioned

  • reinforcement learning
  • potential-based reward shaping
  • vision language model
  • policy learning

Claims Made by the Document

  • VLM-PBRS can learn potential functions for PBRS from VLM feedback.
  • Using smaller, more computationally efficient VLMs reduces costs.
  • Empirical evidence shows that preference labels can still be used to accelerate learning.

Evidence Used

  • The authors describe their method and how it works.
  • The authors present empirical results showing the sample efficiency and robustness of their method.

Methods / Approach

  • VLM-PBRS involves querying a lightweight VLM, training a potential function model using preferences, and employing smaller, more computationally efficient VLMs

Limitations Stated by the Authors

The authors mention that the preference labels may not be as accurate due to using smaller, more computationally efficient VLMs.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not provide information on limitations of the method in other aspects.

Open Questions

  • How does the accuracy of preference labels affect learning performance?
  • Can this method be applied to other reinforcement learning tasks or environments?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper presents an interesting approach for automating potential-based reward shaping in reinforcement learning. However, it's important to note that the preference labels may not be as accurate due to using smaller, more computationally efficient VLMs.

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.