R-GCN-GTG

Source code of "Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning" (AAMAS 2021).

In this work, we show that by leveraging the relational graph of a Relational Graph Convolution Network (R-GCN), we can imbue agents with flexible relational inductive biases. Based on this insight, we propose Grid-to-Graph (GTG), a mapping from grid structures to relational graphs that carry useful spatial relational inductive biases when processed through R-GCN.

The relation determination rules are defined here. The set of relation determination rules are defined here.

Implemented and tested under python 3.6 in Linux. The implementation of IMPALA is based on torchbeast. We modified the monobeast in order to support parallel inference. Logfiles will be stored at ./logs/torchbeast

Installation

1. Install pytorch geometric that implements R-GCN, following the official guide.
2. pip install -r requirements.txt

Run experiments

Run a R-GCN-GTG Agent

python torchbeast/monobeast.py --env=breakout --agent=GCN --xpid=T-1-1

• --bg_code can be used to specific the set of relation determination rules. The default, "b3", is the full GTG with local directional relations, remote directional relations and auxiliary relations in the paper.
• Use a smaller batchsize if it excess the memory limit.

Run a CNN Agent

python torchbeast/monobeast.py --env=breakout --agent=CNN --cnn_code=2c2f --embedding_size=12 --xpid=T-2-1

Run a NLM Agent

python torchbeast/monobeast.py --env=breakout --agent=NLM --xpid=T-3-1 

Citation

@inproceedings{jiang2021gtg,
  title={Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning},
  author={Jiang, Zhengyao and Minervin, Pasquale and Jiang, Minqi and Rockt{\"a}schel, Tim},
  booktitle={AAMAS 2021},
  year={2021}
}