Our proposed approach combines reinforcement learning and deep learning to optimize resource allocation. The reinforcement learning agent learns to predict resource demands based on historical data, while the deep learning model forecasts future resource requirements. The two models are integrated to allocate resources dynamically.
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Our simulation results demonstrate the effectiveness of our approach, with a significant improvement in resource utilization (up to 30%) and cost savings (up to 25%) compared to traditional methods. idmacx v1.9