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
- 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.
- 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"
- 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.
- Run the batch "exec_test.sh"
- change the setting according to the "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".