UoS-PLCCN/pbn-rl

Reinforcement Learning Algorithms for control of Probabilistic Boolean Networks

pbn-rl

Suite of experiments for running Deep Reinforcement Learning for control of Probabilistic Boolean Networks.

Environment Requirements

• CUDA 11.3+
• Python 3.9+

Installation

Local

• Create a python environment using PIP:

  python3 -m venv .env
  source .env/bin/activate

  For the last line, use .\env\Scripts\activate if on Windows.
• Install PyTorch:

  python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113

• Install the package and its dependencies dependencies:

  python -m pip install -r requirements.txt

Running

• Use train_ddqn.py to train a DDQN agent. It's a command line utility so you can check out what you can do with it using --help. E.g.:

  python train_ddqn.py --time-steps 400_000 --env-name n28 --env envs/n28.pkl

• Use train_sb3.py to train a Stable Baselines 3 agent. It's a command line utility so you can check out what you can do with it using --help. E.g.:

  python train_sb3.py --time-steps 400_000 --env-name n28 --env envs/n28.pkl

Getting Help

Principal developer: Evangelos Chatzaroulas (Alternate e-mail).