REST is a reinforcement learning framework for rectilinear Steiner minimum tree (RSMT) construction. Read our paper for more details:

• Jinwei Liu, Gengjie Chen, and Evangeline FY Young. "REST: Constructing Rectilinear Steiner Minimum Tree via Reinforcement Learning." 2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE, 2021.

• Python 3.6+
• PyTorch 1.10.0+
• GeoSteiner 5.1 (included)

Start a new training process for degree 20 by

python3 train.py --degree 20

1. Test the trained model using randomly generated data set by

python3 test.py --degree 20

2. Or use the trained parameters included with this repository

python3 test.py --degree 20 --experiment DAC21

3. As mentioned in the paper, the percentage error can be further reduced by using multiple transformations of the input point set for inference. Inference using all eight transformations by

python3 test.py --degree 20 --experiment DAC21 --transformation 8

4. Lastly, if you want to test the same data set as in the paper

python3 test.py --degree 20 --test_data test_set/test20.txt

Using only one of the transformations for inference

Using all eight transformations for inference

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License Agreement for REST

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