yinjiangjin / HiT-MAC

This repository is the official implementation of Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks.

Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks

This repository is the official implementation of Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks.

📋 Project website: HiT-MAC

Environment

If you want to try your own algorithm in our environment, here is a pure and non-hierarchical DSN environment for you.

Requirements

To install requirements:

pip install -r requirements.txt

Training

To train the executor in the paper, run this command:

python main.py --env Pose-v0 --model single-att --workers 6

To train the coordinator in the paper, run this command:

python main.py --env Pose-v1 --model multi-att-shap --workers 6

Evaluation

To evaluate my model, run:

python main.py --env Pose-v1 --model multi-att-shap --workers 0 --load-coordinator-dir trainedModel/best_coordinator.pth --load-executor-dir trainedModel/best_executor.pth

You can use trained models directly from the folder "trainedModel".

Contributing

If you found this work useful, please consider citing:

📋 Pick a licence and describe how to contribute to your code repository.