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Convergence of Reinforcement Learning and Time-Sensitive Networking for Future Industrial AI Agent Communication: Fundamentals, Challenges, and Opportunities
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姚
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DOI：10.1109/COMST.2026.3690893.png)
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Abstract
En 中文
The rapid proliferation of artificial intelligence in the industrial sector is catalyzing a smart manufacturing paradigm driven by industrial AI agents. In contrast to IT counterparts, future industrial AI agents operate under unique constraints that demand both adaptive intelligence and strictly deterministic communication with ultra-low latency and ultra-high reliability. To address these stringent requirements, the convergence of reinforcement learning (RL) and Time-Sensitive Networking (TSN) has emerged as a critical enabler. This paper presents the fundamentals of RL, TSN, and industrial AI agent communication, and establishes a mapping between AI agent cognitive behaviors, communication requirements, and deterministic networking mechanisms. We propose a four-dimensional framework encompassing intelligent scheduling, dynamic resource management, network convergence and mobility, and application-aware networking to systematically analyze existing RL-for-TSN research and elucidate challenges and requirements for future industrial AI agent communication. Finally, we outline a research roadmap spanning constrained RL algorithms and agent-network co-design to pave the way for deterministic industrial edge intelligence.
Keywords:
Industrial AI agent
time-sensitive networking
reinforcement learning
industrial edge computing
smart manufacturing
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