DAG-NAS: Explainable Neural Architecture Search\\for Reinforcement Learning via Scalar-level DAG Modeling

Authors: Taegun An, Changhee Joo
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: automated machine learning, neural architecture search, directed acyclic graphs, reinforcement learning

Abstract:
We present an explainable and effective Neural Architecture Search (NAS) framework for Reinforcement Learning (RL). We model a feed-forward neural network as a Directed Acyclic Graph (DAG) that consists of scalar-level operations and their interconnections. We train the model for RL tasks using a differentiable search method, followed by pruning the search outcomes. This process results in a compact neural architecture that achieves high performance and enhances explainability by emphasizing crucial information for solving the RL problem. This process results in a compact and efficient neural architecture that enhances explainability by emphasizing crucial information for solving the RL problem. We apply our NAS framework to the Actor-Critic PPO algorithm, targeting both actor and critic networks. We evaluate its performance across various RL tasks. Extensive experiments demonstrate that our architectures achieve comparable performance with significantly fewer parameters while also enhancing explainability by highlighting key features.

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