Edge Computing Reinforcement Learning: Optimizing AI Model Deployments
Keywords:
reinforcement learning, edge computing, resource constraints, model optimizationAbstract
Edge computing installs AI models for data sources to reduce the latency, decision making and bandwidth. This AI model deployment and administration is limited by processing power, memory, and energy. Deployment of this model benefits dynamic and adaptive reinforcement learning which improves resource allocation job scheduling and energy management with environmental requirement. This research paper uses Deep Q Network and proximal policy optimization to merge RL with resource constraint edge computing.
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