Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy

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

Summary

A novel safe transfer reinforcement learning framework is introduced to enhance autonomous highway lane changing decision-making, specifically addressing transfer mismatch and safety during target domain adaptation. This framework incorporates three key mechanisms: an adaptive teacher intervention mechanism that uses instantaneous safety cost to limit risky exploration and generate dual-source samples; a teacher-guided safe transfer module that embeds teacher policy action evaluation into student learning via reward shaping, with guidance diminishing as policy safety improves; and a teacher-guided weighted optimization mechanism that stabilizes transfer performance by adjusting sample weights with a likelihood ratio factor. Validated on varied traffic densities and the real-world NGSIM dataset, the proposed method significantly outperforms baseline approaches, achieving over 52.2% improvement in safety and 5.0% in learning efficiency.

Key takeaway

For Machine Learning Engineers developing autonomous highway lane changing systems, this framework offers a robust approach to mitigate transfer mismatch and enhance safety. You should consider integrating adaptive teacher intervention based on instantaneous safety costs and teacher-guided reward shaping to improve both training safety and learning efficiency. Implementing a weighted optimization mechanism with likelihood ratios can further stabilize your transfer performance, crucial for safety-critical applications.

Key insights

Safe transfer RL for autonomous lane changing uses adaptive teacher guidance to boost safety and efficiency.

Principles

• Transfer mismatch causes training oscillation.
• Safety costs can guide exploration.
• Policy safety can modulate teacher guidance.

Method

The framework employs adaptive teacher intervention for safe exploration, teacher-guided reward shaping for efficiency, and weighted optimization using likelihood ratios to stabilize transfer performance in autonomous lane changing.

In practice

• Apply safety cost for risky exploration.
• Embed teacher action evaluation via reward shaping.
• Adjust sample weights with likelihood ratio.

Topics

• Transfer Reinforcement Learning
• Autonomous Driving
• Lane Changing
• Reward Shaping
• Policy Optimization
• NGSIM Dataset
• Safety-critical AI

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.