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This code is a part of the paper Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks.

Brief instructions

Choose the experiment you want to run, and execute python main.py. Check python main.py --help for additional arguments. If you have a trained model, you can evaluate it with python main.py -eval -load model.pt. For each experiment we include some trained models in the rrl-*/_store/ directory.

The results are logged into stdout and wandb experiment management system. Log-in with your account if necessary.

The code is based on PyTorch and PyTorch Geometric.

Dependencies

The code requires at least Python 3.6.12.

Udpate pip with pip install --upgrade pip. Install pytorch pip install torch==1.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html. Install pytorch geometric requirements: pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html pip install scipy pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html (you can install gpu versions by replacing +cpu with +cu101)

Install other dependencies with pip install -r requirements.txt.

How to train the specific models

BlockWorld

python main.py -boxes <N>

SysAdmin

python main.py -nodes <N> for SysAdmin-S python main.py -nodes <N> --multi for SysAdmin-M

Sokoban

python main.py for default unfiltered dataset (will download automatically) python main.py --custom <XxYxB> for custom size environment of size XxY with B boxes

Evaluate

add -eval -load <model.pt> to the arguments

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Implementation of Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks

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