coalitional_fairness

Coalition Fairness analyzes fairness of actions between groups of Autonomous Vehicles that act selfishly and the traffic effects of a fairness regulation.

Installation

Create virtual environment

conda create -n coalitional_fairness python=3.7
conda activate coalitional_fairness 

Create directory

mkdir coalitional_fairness
cd coalitional_fairness

Clone repository

git clone https://github.com/dianaggomez/coalitional_fairness.git

Install the multiagent High Level Controller environment for training

cd coalitional_fairness/mutliagent
pip install -e .

cd ..
cd ..

Download Copo to use ippo algorithm

git clone https://github.com/decisionforce/CoPO
cd CoPO/copo_code
pip install -e .

Move the following files

cd 
cd coalitional_fairness
move ~/coalitional_fairness/ippo/train_hlc_ippo.py ~/CoPO/copo_code/copo
move ~/coalitional_fairness/ippo/callbacks.py ~/CoPO/copo_code/copo

Folder structure:

coalitional_fairness
├── coalitional_fairness                   
│   ├── multiagent         
│   ├── ippo         
│   └── ...               
└── CoPO

Usage

To train the High Level Controller environment using CoPo ippo algorithm

cd CoPO/copo_code/copo/
python train_hlc_ippo.py --exp-name hlc_ippo --num-gpus=NUM_GPUS

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.