Projected Bellman Operator (PBO)

This is the official code base of the paper Parameterized Projected Bellman Operator, which was presented at the Association for the Advancement of Artificial Intelligence (AAAI 2024).

User installation

Without Docker, with Python 3.8 or 3.9 installed

In the folder where the code is, create a Python virtual environment, activate it, update pip and install the package and its dependencies in editable mode:

python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install -e .

With Docker

Please see the README file made for that.

Run the experiments

All the experiments can be ran the same way by simply replacing the name of the environment, here is an example for LQR.

The following command line runs the training and the evaluation of all the algorithms, one after the other:

launch_job/lqr/launch_local.sh --experiment_name test --max_bellman_iterations 3 --first_seed 1 --last_seed 1

The expected time to finish the runs is 1 minute.

Once all the trainings are done, you can generate the figures shown in the paper by running the jupyter notebook file located at experiments/lqr/plots.ipynb. In the first cell of the notebook, please make sure to change the experiment_name, the max_bellman_iterations and the seeds according to the training that you have ran. You can also have a look at the loss of the training thought the jupyter notebook under experiments/lqr/plots_loss.ipynb.

Run the tests

Run all tests with

pytest

The code should take around 1 minute to run.

Using a GPU

In the folder where the code is, create a Python virtual environment, activate it and install the package and its dependencies in editable mode:

python3 -m venv env_gpu
source env_gpu/bin/activate
pip install -e .
pip install -U jax[cuda11_cudnn82]==0.3.22 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

(Taken from jax-ml/jax#10323)

Using a cluster

Download miniconda on the server host to get Python 3.8:

wget https://repo.anaconda.com/miniconda/Miniconda3-py38_4.12.0-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Install cuda packages with:

conda install -c conda-forge cudatoolkit-dev

do not forget to set the environment variable LD_LIBRARY_PATH correctly. Finally, upgrade pip and install virtualenv

python3 -m pip install --user --upgrade pip
python3 -m pip install --user virtualenv

Now you can go back to the user installation guidelines.