History-aware-Reinforcement-Learning

ABS SAS

Mobile communications case study using autonomous airborne base stations. The system uses RL for positioning the simulated drones while maximising the covered end users.

##User Guide for linux/osx command line

1. Check python version:

python --version  

2. Download anaconda (check python 2 or 3 and select the appropriate Anaconda 2 or 3):

cd /tmp 

curl -O https://repo.anaconda.com/archive/AnacondaX-2019.03-Linux-x86_64.sh 

bash AnacondaX-2019.03-Linux-x86_64.sh 

3. Check installation:

 conda -h

4. Create and activate anaconda environment (“drones_env”) the environment should be created with Python 3.X as interpreter:

conda create --name drones_env python=3.X 

conda activate drones_env 

5. Install numpy

conda install -c anaconda numpy 

6. Install pandas

conda install -c anaconda pandas 

7. Install torch

conda install -c pytorch pytorch 

8. Install matplotlib

conda install -c conda-forge matplotlib 

9. Install paho-mqtt. Check docmuentation about MQTT here

conda install -c conda-forge paho-mqtt

10. Run script

python main.py