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mo-transport-network-design

We introduce Lorenz Conditioned Networks (LCN), a novel multi-policy algorithm for addressing fairness in Multi-Objective Reinforcement Learning (MORL). Based on Lorenz optimality, LCN learns policies that ensure a fair distribution of rewards among different objectives. We extend LCN to introduce $\lambda$-LCN, based on a relaxation of Lorenz optimality that offers flexibility in determining fairness preferences. Finally, we address the lack of real-world MORL benchmarks, by introducing a large-scale, multi-objective environment for real-world transportation network design. Experiments in Xi'an and Amsterdam demonstrate LCN's ability to learn fair policies and scalability in high-dimensional state-action and reward spaces.

Setup

Create the conda environment:

conda env create -f environment.yml

Install the mo-tndp environment:

git submodule update --init --recursive
cd envs/mo-tndp
pip install -e .

Install the morl-baselines repo:

cd morl-baselines
pip install -e .

Install the deep-sea-treasure environment:

python3 -m pip install  deep_sea_treasure

To run GPI-PD: Install pycddlib First install gmp:

brew install gmp

Then, to install the package using pip:

CFLAGS=-I`brew --prefix gmp`/include LDFLAGS=-L`brew --prefix gmp`/lib pip install pip install pycddlib

On Mac M1, if you get an error while installing the morl_baselines, do pip install osqp==0.6.1 and try again (source)

On a linux cluster without sudo permissions, use the following istructions. Firstly, cd to your home directory.

wget https://ftp.gnu.org/gnu/gmp/gmp-6.3.0.tar.bz2
tar -xjf gmp-6.3.0.tar.bz2
cd gmp-6.3.0/
./configure --prefix=/home/YOUR_USER_NAME/opt/

Then, configure the lib and include paths:

export LD_LIBRARY_PATH=/home/YOUR_USER_NAME/opt/lib:$LD_LIBRARY_PATH
export C_INCLUDE_PATH=/home/YOUR_USER_NAME/opt/include:$C_INCLUDE_PATH

And finally, install pycddlib:

CFLAGS=-I/home/dmichai/opt/include LDFLAGS=-L/home/dmichai/opt/lib pip install pycddlib

Reproducing the Experiments

All commands to reproduce the experiments can be found here