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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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Plain-Language Brief

Researchers have developed a new method for reinforcement learning agents using vision language models (VLMs). The approach called VLM-guided PBRS learns a potential function from VLM feedback to address the challenge of sparse rewards in traditional methods. This method preserves original optimal policies, uses smaller and more computationally efficient VLMs, and demonstrates improved sample efficiency and resistance to reward hacking.

What the Document Says

  • The paper introduces a new framework called VLM-PBRS that learns potential functions from vision language model (VLM) feedback for potential-based reward shaping (PBRS).
  • This method aims to address the challenge of sparse rewards in traditional methods by using preferences over image pairs obtained from a lightweight VLM.
  • The preference labels, while less accurate due to using smaller VLMs, are still useful for accelerating learning.
  • Empirical evidence is provided through experiments in Meta-World and Franka Kitchen environments.
  • Three main contributions of the paper are: 1) applying VLM preference-based learning for synthesizing a potential function for PBRS, 2) a low-cost solution that leverages small VLMs, and 3) demonstrating improved sample efficiency and robustness to reward hacking.

Key Concepts Mentioned

  • reinforcement learning
  • potential-based reward shaping
  • vision language models
  • sparse rewards
  • VLM preference labels
  • sample efficiency
  • robotics environments
  • reward hacking

Claims Made by the Document

  • VLM-PBRS learns potential functions from VLM feedback for PBRS.
  • Smaller, more computationally efficient VLMs are used in this approach.
  • Preference labels, though less accurate, can still be used to accelerate learning.
  • Empirical evidence is provided through experiments in Meta-World and Franka Kitchen environments.
  • The method demonstrates improved sample efficiency and robustness to reward hacking.

Evidence Used

  • The text mentions the use of VLM feedback, smaller VLMs, preference labels, empirical evidence, Meta-World, and Franka Kitchen environments.

Methods / Approach

  • VLM-guided PBRS
  • training a model of the potential function using VLM preferences
  • using smaller, more computationally efficient VLMs

Limitations Stated by the Authors

The preference labels generated are less accurate due to the use of smaller VLMs.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not specify any limitations related to data collection, ethical concerns, or generalizability beyond the mentioned environments.

Open Questions

  • What is the long-term performance of VLM-PBRS compared to traditional reinforcement learning methods?
  • How well does this method perform in other domains outside of Meta-World and Franka Kitchen environments?

Notes for Future Q&A

May include interpretation. Labeled where it does.

It should be noted that the text focuses primarily on the method and its empirical validation, so it doesn't provide extensive information about potential long-term performance or generalizability.

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.