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Spectrum sharing in vehicular networks based on multi-agent reinforcement learning, IEEE Journal on Selected Areas in Communications
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# Spectrum sharing in vehicular networks based on multi-agent reinforcement learning L. Liang, H. Ye, and G. Y. Li, "Spectrum sharing in vehicular networks based on multi-agent reinforcement learning," IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2282-2292, Oct. 2019. Trained and tested with Python 3.6 + TensorFlow 1.12.0. How to use the code: - To train the multi-agent RL model: main_marl_train.py + Environment_marl.py + replay_memory.py - To train the benchmark single-agent RL model: main_sarl_train.py + Environment_marl.py + replay_memory.py - To test all models in the same environment: main_test.py + Environment_marl_test.py + replay_memory.py + '/model'. - Figures 3 and 4 in the paper can be directly reproduced from running "main_test.py". Change the V2V payload size by "self.demand_size" in "Environment_marl_test.py". - Figure 5 can only be obtained from recording returns during training. - Figures 6-7 show performance of an arbitrary episode (but with failed random baseline and successful MARL transmission). In fact, most of such episodes exhibit some interesting observations demonstrating multi-agent cooperation. Interpretation is up to the readers. - Use of "Test" mode in "main_marl_train.py" is not recommended. **NOTE: Kai Huang has released a more refined repo of this project, written in PyTorch, which runs much faster than this TensorFlow version. Please check it out here: https://github.com/huangkk99/MARLV2X**
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4de06fc4a043d5a6, llm:Description: "Spectrum sharing in vehicular networks based on multi-agent reinforcement learning" (IEEE Journal). Repository language: Python.
matched fp:4de06fc4a043d5a6, llm:Description: "Spectrum sharing in vehicular networks based on multi-agent reinforcement learning" (IEEE Journal). Repository language: Python.