Pytorch implementation of "Communication-Assisted Multi-Agent Reinforcement Learning Improves Task-Offloading in UAV-Aided Edge-Computing Networks"
This is the github repo for the work "Communication-Assisted Multi-Agent Reinforcement Learning Improves Task-Offloading in UAV-Aided Edge-Computing Networks".
Please refer to https://github.com/chenbq/CAVDN/blob/main/Additional%20Experiments.pdf
Detailed settings regarding the neural networks and the training process for the paper are provided as follows.
The FNN in encode module and intention module have three hidden layers with [128, 128, 64] neurons. The RNN has one hidden layer with 64 neurons. The FNN in combine module has two hidden layers having [128, 128] neurons. For all networks,
Key parameters to train the models
Parameters | Value |
---|---|
25 | |
1 | |
0.5 | |
0.5 | |
Buffer Size | |
32 | |
0.95 | |
0.001 |
The complexity of CAVDN can be reflected by the number of parameters of the agent network. Therefore, we analyze the number of parameters to discuss the complexity of the algorithm.
Let
- To train the model. run main.py The hyper-parameters can be set as in /common/arguments.py
- To produce the reward figure. run plot_reward_all.py
- To produce the test figure. run main_eval.py