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
- Test the trained model using randomly generated data set by
python3 test.py --degree 20
- Or use the trained parameters included with this repository
python3 test.py --degree 20 --experiment DAC21
- 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
- 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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