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.

Please send all questions/inquires to [email protected].

I'm now a Professor in Southeast University (SEU), Nanjing, China. Please drop me an email if you're interested in working with me.

Our research website: www.liang-seu.net