Bin Packing Problem using Neural Combinatorial Optimization.
This Tensorflow model tackles Bin-Packing Problem using Reinforcement Learning. It trains multi-stacked LSTM cells to perform an RNN agent able to embed information from the environment and variable size sequences batched form the whole combinational input space.
This AI is performed to behave like a first-fit algorithm. https://en.wikipedia.org/wiki/Bin_packing_problem#First-fit_algorithm
My special greetings to Michel Deudon (@mdeudon) & Pierre Cournut (@pcournut) for their inspirational TSP implementation. https://github.com/MichelDeudon/neural-combinatorial-optimization-rl-tensorflow
- Python 3.6
- Tensorflow 1.8.0
- Minizinc 2.1.1 (optional -> --enable_performance)
pip install -r requirements.txt
Test pretained model performance:
python main.py --train_mode=False --load_model=True (--enable_performance=True)
Train your own model from scratch:
python main.py --train_mode=True --save_model=True
Continue training a previously saved model:
python main.py --train_mode=True --save_model=True --load_model=True
To visualize training variables on Tensorboard:
tensorboard --logdir=summary/repo
To activate Tensorflow debugger in Tensorboard, uncomment TensorBoard Debug Wrapper code. Execute Tensorboard after running the model.
tensorboard --logdir=summary/repo --debugger_port 6064
Solutions are tested againt Gecode open-source constraint solver.
Performance obtained is over 80%.
Ruben Solozabal, PhD student at the University of the Basque Country [UPV/EHU] Bilbao
Date: October 2018
Contact me: [email protected]
Bello, I., Pham, H., Le, Q. V., Norouzi, M., & Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940.
Azalia Mirhoseini, Hieu Pham, Quoc Le, Mohammad Norouzi, Samy Bengio, Benoit Steiner, Yuefeng Zhou, Naveen Kumar, Rasmus Larsen, and Jeff Dean, Device placement optimization with reinforcement learning, 2017.