Multi Agent Deep Reinforcement Learning
The proposed method is rigorously evaluated against several state-of-the-art algorithms, including ISAC, ITD3, IPPO, and IDDPG, to ensure a comprehensive performance analysis. The experimental data, which is publicly available [here], provides detailed insights into the training and evaluation processes of each algorithm.
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This repository includes the DDPG, MADDPG, HHCDA, and MAHHCDA based on the paper "AI-Based and Mobility-aware Energy Efficient Resource Allocation and Trajectory Design for NFV enabled Aerial Networks".
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