Lipschitz-Bounded Policy Networks for RL in MJX

This repository contains the code used to produce the results in Section 4.1 of the paper: On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks. See here for the code used to produce the results in Section 4.2.

The code in this repository has been structured so that it is extensible to training and evaluating Lipschitz-bounded policies on any other environment in MJX. Please feel free to install it and play around with your favourite robotic environments or re-create the figures from our paper.

Installation

This code is based on MuJoCo's JAX implementation, MJX, and the RL libraries in Brax. There are two ways to install all the dependencies. We recommend the following as the easiest way to get started:

• Use a local install in a virtual environment for all development and results analysis.
• Use the docker image for training models across multiple GPUs on a server/cluster/different workstation.

System dependencies

All code was tested and developed in Ubuntu 22.04 with CUDA 12.3 and Python 3.10.12. To run any of the code in this repository, you will need a CUDA-compatible NVIDIA GPU.

Using Python virtual environments

Create a Python virtual environment and activate it:

python -m venv venv
source venv/bin/activate

Install all the dependencies and the project package itself from within the project root directory.

pip install --upgrade pip
pip install -r requirements.txt
pip install -e .

The third line installs the local package liprl itself. The requirements.txt file was generated with pipreqs. Most importantly, you will need to set up JAX to find and use your NVIDIA GPU with the following.

pip install -U "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

All plots use LaTeX font as the default. This requires a local install of LaTeX. Note that the default distribution of LaTeX on Ubuntu is not enough. Install the extra packages with the following:

sudo apt update && sudo apt upgrade
sudo apt install texlive-full

Using Docker

Build the docker container from the project root directory (this might take a while):

cd <repo/root/directory/>
docker build -t lipschitz_rl docker/

This will install all dependencies (including CUDA). Training scripts can then be run with

./docker/scripts/train_attack_pendulum.sh

Note that you will first need to specify your local path to the repository in the training script.

Repository structure

The repository is structured as follows:

• docker/: contains the Dockerfile to build the docker image, and also contains useful scripts with which to run training experiments from within the docker image.

• liprl/: contains all the tools used to train models, run experiments, collect data, and analyse results wrapped up in a (locally) pip-installable package.

• results/: contains all trained models and results:

  • results/paper-plots/: contains all figures included in Section 4.1 of the paper.
  • results/pendulum: contains trained models and adversarial attack/sample delay data used to create the plots in results/paper-plots/.

• scripts/pendulum/: contains scripts for running and analysing experiments on the pendulum environment. Some explanation on the naming convention below:

  • All scripts with the prefix train_ re-generate results that are already saved in the results/pendulum/ folder. Eg: code is provided to tune, train, and attack pendulum swing-up policies.
  • All scripts with the pre-fix analysis_ re-generate the figures in the results/paper-plots/ folder.

If you would like to re-generate figures from the paper or re-train any models, please run the scripts in the order they are indexed in the scripts/pendulum/ directory.

A note on naming conventions

Section 4.1 of the paper compares unconstrained MLP policies with Lipschitz-bounded policies. Throughout the code, these are referred to as MLP and LBDN (respectively). The term LBDN stands for Lipschitz-Bounded Deep Network. All LBDN networks are composed from Sandwich layers.

Contact

Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.