An Integrated Approach to Neural Architecture Search for Deep Q-Networks

• URL: http://arxiv.org/abs/2510.19872v1
• Date: Wed, 22 Oct 2025 07:17:31 GMT
• Title: An Integrated Approach to Neural Architecture Search for Deep Q-Networks
• Authors: Iman Rahmani, Saman Yazdannik, Morteza Tayefi, Jafar Roshanian,
• Abstract summary: NAS-DQN is an agent that integrates a learned neural architecture search controller directly into the DRL training loop.<n>We evaluate NAS-DQN against three fixed-architecture baselines and a random search control on a continuous control task.<n>Our results demonstrate that NAS-DQN achieves superior final performance, sample efficiency, and policy stability while incurring negligible computational overhead.
• Score: 1.516610503825416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The performance of deep reinforcement learning agents is fundamentally constrained by their neural network architecture, a choice traditionally made through expensive hyperparameter searches and then fixed throughout training. This work investigates whether online, adaptive architecture optimization can escape this constraint and outperform static designs. We introduce NAS-DQN, an agent that integrates a learned neural architecture search controller directly into the DRL training loop, enabling dynamic network reconfiguration based on cumulative performance feedback. We evaluate NAS-DQN against three fixed-architecture baselines and a random search control on a continuous control task, conducting experiments over multiple random seeds. Our results demonstrate that NAS-DQN achieves superior final performance, sample efficiency, and policy stability while incurring negligible computational overhead. Critically, the learned search strategy substantially outperforms both undirected random architecture exploration and poorly-chosen fixed designs, indicating that intelligent, performance-guided search is the key mechanism driving success. These findings establish that architecture adaptation is not merely beneficial but necessary for optimal sample efficiency in online deep reinforcement learning, and suggest that the design of RL agents need not be a static offline choice but can instead be seamlessly integrated as a dynamic component of the learning process itself.

Related papers

• Relatron: Automating Relational Machine Learning over Relational Databases [50.94254514286021]
  We present a study that unifies RDL and DFS in a shared design space and conducts architecture-centric searches across diverse RDB tasks.<n>Our analysis yields three key findings: (1) RDL does not consistently outperform DFS, with performance being highly task-dependent; (2) no single architecture dominates across tasks, underscoring the need for task-aware model selection; and accuracy is an unreliable guide for choice architecture.
  arXiv  Detail & Related papers  (2026-02-26T02:45:22Z)
• Deep Hierarchical Learning with Nested Subspace Networks [53.71337604556311]
  We propose Nested Subspace Networks (NSNs) for large neural networks.<n>NSNs enable a single model to be dynamically and granularly adjusted across a continuous spectrum of compute budgets.<n>We show that NSNs can be surgically applied to pre-trained LLMs and unlock a smooth and predictable compute-performance frontier.
  arXiv  Detail & Related papers  (2025-09-22T15:13:14Z)
• DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation [41.63918697375821]
  We propose DANCE (Dynamic Architectures with Neural Continuous Evolution), which reformulates architecture search as a continuous evolution problem.<n>DANCE introduces three key innovations: a continuous architecture distribution enabling smooth adaptation, a unified architecture space with learned selection gates for efficient sampling, and a multi-stage training strategy for effective deployment optimization.<n>Our method consistently outperforms state-of-the-art NAS approaches in terms of accuracy while significantly reducing search costs.
  arXiv  Detail & Related papers  (2025-07-07T05:22:55Z)
• Auto-Compressing Networks [51.221103189527014]
  We introduce Auto-compression Networks (ACNs), an architectural variant where long feedforward connections from each layer replace traditional short residual connections.<n>We show that ACNs exhibit enhanced noise compared to residual networks, superior performance in low-data settings, and mitigate catastrophic forgetting.<n>These findings establish ACNs as a practical approach to developing efficient neural architectures.
  arXiv  Detail & Related papers  (2025-06-11T13:26:09Z)
• Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL) [43.108040967674185]
  This paper introduces Evolutionary Multi-Objective Network Architecture Search (EMNAS) for the first time to optimize neural network architectures in large-scale Reinforcement Learning for Autonomous Driving (AD)<n> EMNAS uses genetic algorithms to automate network design, tailored to enhance rewards and reduce model size without compromising performance.
  arXiv  Detail & Related papers  (2025-06-10T07:52:35Z)
• ZeroLM: Data-Free Transformer Architecture Search for Language Models [54.83882149157548]
  Current automated proxy discovery approaches suffer from extended search times, susceptibility to data overfitting, and structural complexity.<n>This paper introduces a novel zero-cost proxy methodology that quantifies model capacity through efficient weight statistics.<n>Our evaluation demonstrates the superiority of this approach, achieving a Spearman's rho of 0.76 and Kendall's tau of 0.53 on the FlexiBERT benchmark.
  arXiv  Detail & Related papers  (2025-03-24T13:11:22Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• Robustifying and Boosting Training-Free Neural Architecture Search [49.828875134088904]
  We propose a robustifying and boosting training-free NAS (RoBoT) algorithm to develop a robust and consistently better-performing metric on diverse tasks. Remarkably, the expected performance of our RoBoT can be theoretically guaranteed, which improves over the existing training-free NAS.
  arXiv  Detail & Related papers  (2024-03-12T12:24:11Z)
• Training-free Neural Architecture Search for RNNs and Transformers [0.0]
  We develop a new training-free metric, named hidden covariance, that predicts the trained performance of an RNN architecture. We find that the current search space paradigm for transformer architectures is not optimized for training-free neural architecture search.
  arXiv  Detail & Related papers  (2023-06-01T02:06:13Z)
• FreeREA: Training-Free Evolution-based Architecture Search [17.202375422110553]
  FreeREA is a custom cell-based evolution NAS algorithm that exploits an optimised combination of training-free metrics to rank architectures. Our experiments, carried out on the common benchmarks NAS-Bench-101 and NATS-Bench, demonstrate that i) FreeREA is a fast, efficient, and effective search method for models automatic design.
  arXiv  Detail & Related papers  (2022-06-17T11:16:28Z)
• Neural Architecture Generator Optimization [9.082931889304723]
  We are first to investigate casting NAS as a problem of finding the optimal network generator. We propose a new, hierarchical and graph-based search space capable of representing an extremely large variety of network types.
  arXiv  Detail & Related papers  (2020-04-03T06:38:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.