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Applying Machine Learning (ML) techniques to design and optimize computerarchitectures is a promising research direction. Optimizing the runtimeperformance of a Network-on-Chip (NoC) necessitates a continuous learningframework. In this work, we demonstrate the promise of applying reinforcementlearning (RL) to optimize NoC runtime performance. We present three RL-basedmethods for learning optimal routing algorithms. The experimental results showthe algorithms can successfully learn a near-optimal solution across differentenvironment states. Reproducible Code:github.com/huckiyang/interconnect-routing-gym

Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization