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DAG-NAS : An explainable neural architecture search framework for reinforcement learning.

Taegun An1, Changhee Joo1

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Neural Networks : the Official Journal of the International Neural Network Society
|April 8, 2026
PubMed
Summary

We developed an explainable Neural Architecture Search (NAS) framework for Reinforcement Learning (RL). Our method creates compact, high-performing, and interpretable neural networks by modeling them as Directed Acyclic Graphs (DAGs).

Keywords:
Directed acyclic graphExplainable architecture searchNeural architecture searchReinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Neural Architecture Search (NAS) is crucial for optimizing deep learning models.
  • Explainability in Reinforcement Learning (RL) remains a significant challenge.
  • Current NAS methods often produce complex, black-box models.

Purpose of the Study:

  • To introduce an explainable NAS framework for Reinforcement Learning (RL).
  • To enhance the interpretability of neural network architectures in RL.
  • To develop compact and high-performance RL models.

Main Methods:

  • Modeling feed-forward neural networks as Directed Acyclic Graphs (DAGs) at the scalar level.
  • Employing a differentiable search method for training DAGs.
  • Implementing a pruning strategy on search results to refine architectures.

Main Results:

  • The NAS framework successfully identified compact neural architectures.
  • Achieved comparable performance to existing methods with significantly fewer parameters.
  • Demonstrated enhanced explainability by highlighting critical information pathways.

Conclusions:

  • The proposed NAS framework effectively balances performance, parameter efficiency, and explainability in RL.
  • Scalar-level DAG representation offers a promising direction for interpretable AI.
  • The method is validated across various RL tasks, including Actor-Critic PPO.