https://github.com/instadeepai/alphanpi

Adapting the AlphaZero algorithm to remove the need of execution traces to train NPI.
https://github.com/instadeepai/alphanpi

deep-learning deep-reinforcement-learning machine-learning neural-networks neural-program-synthesis neural-programmer-interpreter reinforcement-learning

Last synced: 8 months ago
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Adapting the AlphaZero algorithm to remove the need of execution traces to train NPI.

• Host: GitHub
• URL: https://github.com/instadeepai/alphanpi
• Owner: instadeepai
• License: mit
• Created: 2019-05-27T10:06:28.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-10-03T22:41:42.000Z (over 2 years ago)
• Last Synced: 2023-10-20T20:24:18.905Z (over 2 years ago)
• Topics: deep-learning, deep-reinforcement-learning, machine-learning, neural-networks, neural-program-synthesis, neural-programmer-interpreter, reinforcement-learning
• Language: Python
• Homepage:
• Size: 49.8 KB
• Stars: 76
• Watchers: 9
• Forks: 15
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# AlphaNPI

Adapting the AlphaZero algorithm  to remove the need of execution traces to train NPI.

## Setup

You need to install the required Python packages.

    cd alphanpi/

Then run the command:

    pip install -r requirements.txt

Update the following environment variable:

    export PYTHONPATH=$PWD:$PYTHONPATH

## Training

    cd trainings/

Run one of the scripts:

    python train_recursive_sorting.py --tensorboard --verbose --save-model --save-results --save-model

    python train_hanoi.py --tensorboard --verbose --save-model --save-results --save-model

    python train_sorting_nohierarchy.py --tensorboard --verbose --save-model --save-results --save-model

    python train_hanoi.py --tensorboard --verbose --save-model --save-results --save-model

For more information about the arguments that can be sent, see at trainings/README.md

    

## Validation

The following allows to assert the results disclosed in the paper

    cd validation/

Then run one of the scripts:

    python validate_hanoi.py --verbose --save-results

    python validate_recursive_sorting.py --verbose --save-results

    python validate_sorting.py --verbose --save-results

    python validate_sorting_nohierarchy.py --verbose --save-results

For more information about the arguments that can be sent, see at validation/README.md

    

## Visualization

    cd visualization/

To visualize a pre-trained model **behavior** for an environment **env**, run the script **visualize_{env}.py**. Set the load path at the beginning of the script to the path where the model of interest is saved.

Run one of the scripts:

    python visualize_hanoi.py

    python visualize_recursive_sorting.py

    python visualize_sorting.py

    python visualize_sorting_nohierarchy.py

    

    

### Generate visualization

When a visualization script is executed, it generates a **mcts.gv** file under visualization/. The file contains a description of the tree in dot language. 

If you don't already have graphviz installed, run command:

    sudo apt-get install python3-pydot graphviz

To convert the .gv in .pdf file, use command:

    dot -Tpdf mcts.gv -o mcts.pdf