Automating Potential-based Reward Shaping with Vision Language Model Guidance

2026-06-25 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

The VLM-guided PBRS (VLM-PBRS) framework automates potential-based reward shaping (PBRS) in reinforcement learning, addressing the challenge of sparse rewards without inducing reward hacking. This method learns the potential function directly from Vision Language Model (VLM) feedback, specifically by querying a lightweight VLM for preferences over image pairs and then training a model of the potential function. VLM-PBRS preserves the original optimal policies and removes the need for expert-designed reward shaping terms. To manage computational costs, it employs smaller, more efficient VLMs, which, despite less accurate preference labels, still accelerate learning. The framework was empirically validated in Meta-World and Franka Kitchen environments, demonstrating improved sample efficiency and robustness to reward hacking. This represents the first application of VLM preference-based learning to synthesize a potential function for PBRS.

Key takeaway

For Machine Learning Engineers developing reinforcement learning agents in environments with sparse rewards, VLM-PBRS offers a principled solution to accelerate learning without risking reward hacking. You can utilize lightweight Vision Language Models to automatically generate potential functions, eliminating the need for manual expert-designed shaping. Consider integrating this framework to improve sample efficiency and maintain optimal policy sets in your projects.

Key insights

VLM-PBRS automates potential-based reward shaping using lightweight VLM preferences to accelerate RL without policy degradation.

Principles

• PBRS guarantees optimal policy preservation.
• Small VLMs can accelerate RL despite lower accuracy.
• Preference-based learning synthesizes potential functions.

Method

Query a lightweight VLM for image pair preferences. Train a potential function model using these preferences. Integrate this learned potential function into PBRS for reward shaping.

In practice

• Apply VLM-PBRS in sparse reward RL tasks.
• Use lightweight VLMs for cost-effective reward shaping.
• Validate against Meta-World or Franka Kitchen benchmarks.

Topics

• Reinforcement Learning
• Reward Shaping
• Vision Language Models
• Potential-Based Rewards
• Sample Efficiency
• Meta-World

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.