NOML-NOML: hierarchical TD3 + anchor policy for flight control [P]

2026-05-20 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Advanced, quick

Summary

NOML-NOML is a custom Reinforcement Learning algorithm designed for continuous flight control in a 6-DoF flight simulator, addressing limitations encountered with vanilla TD3. The author developed NOML-NOML after observing that standard TD3 implementations would peak and then collapse into persistent pitch oscillation. The algorithm integrates three key structural modifications into a TD3 skeleton: an "anchor policy" that combines a fixed safe action with a delta-gated policy, preventing catastrophic policy forgetting; a "hierarchical actor" comprising three independent MLPs for pitch, roll, and other controls, each with its own optimizer, which effectively eliminated the observed oscillation; and "mirror learning" to generate double the training data by leveraging left-right environmental symmetry. Notably, the research found that exploration noise was largely detrimental, with the anchor policy providing sufficient safe behavior fallback. The Apache 2.0 licensed code is open-sourced.

Key takeaway

For Machine Learning Engineers developing robust continuous control agents for complex physical systems, vanilla TD3 may prove insufficient due to structural instability like oscillation. You should consider implementing architectural modifications such as a hierarchical actor to decouple control axes and an anchor policy to ensure safe fallback behavior. Additionally, utilize environmental symmetries through mirror learning to efficiently double your training data, potentially reducing the need for extensive exploration noise.

Key insights

Hierarchical actors, anchor policies, and mirror learning stabilize continuous control RL, preventing collapse and improving data efficiency.

Principles

• Structural RL issues often demand structural algorithm changes.
• Decouple control axes to prevent gradient corruption.
• Exploit environmental symmetry for data augmentation.

Method

Integrate an `anchor + delta·gate` action policy, a hierarchical actor with independent MLPs for pitch, roll, and other controls, and mirror learning for data augmentation into a TD3 framework.

In practice

• Apply anchor policies for robust continuous control.
• Decouple control axes with hierarchical actors.
• Utilize symmetry for 2x data efficiency.

Topics

• Reinforcement Learning
• Continuous Control
• TD3
• Flight Control
• Hierarchical RL
• Data Augmentation
• Anchor Policy

Code references

• 9138noms/NOML

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.