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[IEEE TSIPN' 2022] "Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks", by Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, and Brian M. Sadler

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Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

License: MIT IEEE Transactions on Signal and Information Processing over Networks, 2022, Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

The code is used to reproduce the result of "Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks"

Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro and Brian M. Sadler

Prerequisite

  • Before running the script, make sure you have install microsoft airsim on 4.23 version. The instruction for the installation is in https://microsoft.github.io/AirSim/
  • After installing airsim, launching the airsim by GUI and play start. Make sure the setting.json is correctly located in the airsim folder.
  • Pytorch 1.0.0
  • If you would like to get the environment we use for airsim, please replace the project file with ours in airsim folder.

Quick start

  • Run the batch file "exec_dagnn.sh"/"exec_grnn.sh"" for the training.
  • replace the path of 'root_path' to change the path to store the dataset.
  • change the setting according to the "Training hyper-parameters"

Training hyper-parameters

  • n_times : how many steps for each trajectory.
  • n_agents : number of agents for the group.
  • n_exp : number of training data for the initial size of dataset.
  • start_idx : set 0 for the starting point of each trajectory.
  • filterLength : the temporal length for DAGNN/GRNN.
  • n_vis : number of feature dimension for transmission.
  • radius : radius for the disk model.
  • vinit : the maximum velocity for initialization.
  • mode : 'optimal' using centralized controller to collect ground truth. 'vis_grnn'/'vis_dagnn'/ using other controllers to collect ground truth.
  • arch: 'vis_grnn'/'vis_dagnn' for the choice of archetecture.
  • comm_model : 'disk' for disk model. 'knn' for knn model.
  • n_exp_aug : number of datasize for the augmentation of dataset.

Quick test

  • Run the batch "exec_test.sh"
  • change the setting according to the "Testing hyper-parameters"

Testing hyper-parameters

  • n_agents : number of agents for each trajectory
  • arch : 'vis_grnn'/'vis_dagnn' for the choice of archetecture.
  • seed : the random seed for the initialization.
  • Other hyper-parameters in "Training hyper-parameters".

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[IEEE TSIPN' 2022] "Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks", by Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, and Brian M. Sadler

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