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[2505.10992] ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networks
Summary
The paper presents ReaCritic, a novel reasoning transformer-based critic model for deep reinforcement learning (DRL) in heterogeneous wireless networks, enhancing decision-making capabilities.
Why It Matters
As wireless networks become increasingly complex, traditional DRL methods struggle with adaptability. ReaCritic addresses this by integrating reasoning capabilities, improving performance and convergence in dynamic environments, which is crucial for effective network management.
Key Takeaways
- ReaCritic introduces reasoning capabilities to DRL critic models.
- It enhances adaptability in heterogeneous network environments.
- The model improves convergence speed and performance in various tasks.
- Compatible with a wide range of DRL algorithms.
- Demonstrated effectiveness through extensive experimental results.
Computer Science > Machine Learning arXiv:2505.10992 (cs) [Submitted on 16 May 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networks Authors:Feiran You, Hongyang Du View a PDF of the paper titled ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networks, by Feiran You and 1 other authors View PDF HTML (experimental) Abstract:Heterogeneous Networks (HetNets) pose critical challenges for intelligent management due to the diverse user requirements and time-varying wireless conditions. These factors introduce significant decision complexity, which limits the adaptability of existing Deep Reinforcement Learning (DRL) methods. In many DRL algorithms, especially those involving value-based or actor-critic structures, the critic component plays a key role in guiding policy learning by estimating value functions. However, conventional critic models often use shallow architectures that map observations directly to scalar estimates, limiting their ability to handle multi-task complexity. In contrast, recent progress in inference-time scaling of Large Language Models (LLMs) has shown that generating intermediate reasoning steps can significantly improve decision quality. Motivated by this, we propose ReaCritic, a reasoning transformer-based critic-model scaling scheme that brings reasoning-like ability into DRL. ReaCritic performs horizontal reasoning over parallel...