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.