Code accompanying the paper:

Uncovering Instabilities in Variational-Quantum Deep Q-Networks. Maja Franz, Lucas Wolf, Maniraman Periyasamy, Christian Ufrecht, Daniel D. Scherer, Axel Plinge, Christopher Mutschler, Wolfgang Mauerer. Journal of The Franklin Institute. Elsevier (Open Access). 2022.

To train an agent run:

python3 train.py path.to.config

e.g.

python3 train.py tfq.reproduction.skolik.cartpole.skolik_hyper.baseline.gs.sc_enc

We provide several configuration files in configs/

A training process can be tracked with TensorBoard:

tensorboard --logdir logs --host 0.0.0.0

We provide all scripts and data, which were used to create the graphs in the paper.

Generate all figures:

cd plot/
./plot_all.sh 

Plot a single or multiple conducted experiments:

cd plot/
./plot_runs.sh

The Dockerimages contain all neccessary libraries and packages to run the training and plotting scripts. We provide one GPU and one CPU-only version.

docker build -f Dockerfile_GPU -t quantum-rl-gpu .

or

docker build -f Dockerfile_CPU -t quantum-rl-cpu .

docker run --name qrl-gpu -it -d --runtime=nvidia -v $PWD:/home/repro -p 6006:6006 quantum-rl-gpu

or

docker run --name qrl-cpu -it -d -v $PWD:/home/repro -p 6006:6006 quantum-rl-cpu

docker exec -it qrl-cpu /bin/bash

or

docker exec -it qrl-cpu /bin/bash

Restart a stopped container:

docker start qrl-gpu

or

docker start qrl-cpu