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DSA_DRQN_updated
power-control-uora-11be
11be_simulator
DSA_DRQN
Multi-com
This is the companion repository for the following paper. Trained and tested with Python 3.6 + TensorFlow 1.12.0. S. Zhang, Z. Ni, and L. Kuang, "Traffic Priority-aware Multi-user Distributed Dynamic Spectrum Access: A Multi-agent Deep RL Approach" How to use the code: - To train the MARL-more model: train_more.py + env_network.py + Replay_memory.py - To train the MARL-one model: train_one.py + env_network.py + Replay_memory.py - To test all models in the same environment: test_more_vs_one.py +env_network.py+ '/model'. Please send all questions/inquires to syzhang13@163.com We thank the author MorvanZhou for his RL class codes and the author Le Liang for his paper "Spectrum sharing in vehicular networks based on multi-agent reinforcement learning" codes, which are available at: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow https://github.com/le-liang/MARLspectrumSharingV2X
AoI-Optimal-Dynamic-P-ower-C-ontrol-for-IoT-networks-A-DRDPG-Approach
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