Trajectory-Level Data Augmentation for Offline Reinforcement Learning

2026-05-14 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, long

Summary

A new data augmentation method, LIFT (logging improvement via fine-tuned trajectories), is proposed for offline reinforcement learning, specifically targeting active positioning problems. LIFT enables training off-policy models from limited, suboptimal trajectories by exploiting task structure and geometric relationships between rewards, value functions, and logging policies. The method introduces a trajectory-based augmentation technique that identifies "shortcuts" in logged data, allowing an augmentor to skip redundant sub-trajectories and smooth hand-offs with the logging policy during data collection. This approach supports suboptimal logging policies, leading to higher data quality and improved offline RL performance. The framework is theoretically justified and empirically validated across various positioning tasks, including those with partial observability and varying dimensionality, and is implemented in d3rlpy for easy integration.

Key takeaway

For research scientists developing offline reinforcement learning solutions for real-world control systems, LIFT offers a principled way to enhance data quality from suboptimal logging policies. You should consider integrating this trajectory-level augmentation to improve model performance, especially in scenarios with costly online exploration or where logging policies are deterministic and structured, as it can significantly reduce extrapolation errors and lead to more robust policies.

Key insights

LIFT augments suboptimal offline RL trajectories by identifying geometric "shortcuts" to improve data quality and learning.

Principles

• Offline RL performance depends on logging policy quality.
• Exploit geometric structure to identify trajectory shortcuts.
• Augmentors can skip redundant sub-trajectories.

Method

LIFT trains an augmentor during data collection to identify and execute "shortcuts" in suboptimal trajectories, leveraging geometric structure to find higher-value states and smooth hand-offs with the logging policy.

In practice

• Integrate LIFT into d3rlpy with a single line of code.
• Apply to active positioning tasks like lens alignment.
• Use with deterministic, scripted logging policies.

Topics

• Offline Reinforcement Learning
• Trajectory Data Augmentation
• LIFT Framework
• Active Positioning Problems
• Logging Policies

Code references

• HS-Kempten/lift

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 stat.ML updates on arXiv.org.