mlsanityData to 14 Oct 2026

Transformer Actor-Critic for Efficient Freshness-Aware Resource Allocation

arXiv 2602.22774 ↗First posted 26 Feb 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Emerging applications such as autonomous driving and industrial automation demand ultra-reliable and low-latency communication (URLLC), where maintaining fresh and timely information is critical. A key performance metric in such systems is the age of information (AoI). This paper addresses AoI minimization in a multi-user uplink wireless network using non-orthogonal multiple access (NOMA), where users offload tasks to a base station. The system must handle user heterogeneity in task sizes, AoI thresholds, and penalty sensitivities, while adhering to NOMA constraints on user scheduling. We propose a deep reinforcement learning (DRL) framework based on proximal policy optimization (PPO), enhanced with a Transformer encoder. The attention mechanism allows the agent to focus on critical user states and capture inter-user dependencies, improving policy performance and scalability. Extensive simulations show that our method reduces average AoI compared to baselines. We also analyze the evolution of attention weights during training and observe that the model progressively learns to prioritize high-importance users. Attention maps reveal meaningful structure: early-stage policies exhibit uniform attention, while later stages show focused patterns aligned with user priority and NOMA constraints. These results highlight the promise of attention-driven DRL for intelligent, priority-aware resource allocation in next-generation wireless systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 16 references that are in the map. First posted on arXiv 26 Feb 2026.

Builds on

7 of 16 references.

References · 7

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2021AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learningcited by 18
  4. 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 13
  5. 2019Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspectivecited by 10
  6. 2023AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertaintycited by 1
  7. 2024Mean Age of Information in Partial Offloading Mobile Edge Computing Networkscited by 1

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  1. 2026Diffusion Model-based Reinforcement Learning for Version Age of Information Scheduling: Average and Tail-Risk-Sensitive Control
  2. 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning
  3. 2025Toward Trustworthy Digital Twins in AI Agent-based Wireless Network Optimization: Challenges, Solutions, and Opportunities
  4. 2022Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning
  5. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
  6. 2024AoI-Aware Resource Allocation for Smart Multi-QoS Provisioning
  7. 2025Policy Gradient Algorithms for Age-of-Information Cost Minimization
  8. 2026Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks