Some path schedule solutions specifically for UAV-sensorTime-sensitive Network. The code is used to evaluate the models' performance and time complexity with three different algorithms. Especially, establishing the DQN solving.

• length_range: The range of map's length and width.
• priority_range: The range of possible sensors' priority.
• s:
• v: The speed of UAV.
• period: One period named t in the paper.
• t_limit: The working-time limitation which is caused by fuel.
• max_time: The total time the system run.
• seed: Random number seed for generate random sensors lists.

1. Install Python and dependencies with Miniconda

   $ conda -V
   conda 4.7.10
   

2. Install Python and dependencies with conda.

   $ conda env create -f environment.yml
   $ conda activate UAV
   (UAV) $ python -V
   Python 3.7.3
   

   or

   $ conda env update -f environment.yml
   

3. Run Python script. The results save at ./out/

   (UAV) $ python greedy.py
   (UAV) $ python QL.py
   (UAV) $ python DQN.py
   

4. If you wanna remove conda environment:

   (UAV) $ conda deactivate
   $ conda remove -n UAV --all