Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions.
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