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Repo with scripts and documentation for the TMLR paper: "Towards a General Transfer Approach for Policy-Value Networks"

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Towards a General Transfer Approach for Policy-Value Networks

This repository contains supporting scripts used for the paper "Towards a General Transfer Approach for Policy-Value Networks". The scripts in this repository are only fairly small scripts, defining experiments that were run and providing links between Ludii and Polygames.

Dependency: Ludii (general game playing engine)

The Ludii repository contains:

  • Game description files (defining the rules of all games used in this paper, interpreted by Ludii's engine)
  • Code to generate tensors from Ludii's internal state action action representations, return rewards, and any other code that is required by the Python-based deep learning code. More specifically, this code is implemented in the LudiiGameWrapper.java and LudiiStateWrapper.java files.
  • Code to identify which channels should map to each other for any given source-target pairing. This is implemented in the moveTensorSourceChannels() and stateTensorSourceChannels() methods of LudiiGameWrapper.java.

Dependency: Polygames (search & deep learning code)

The Polygames repository contains:

Citing Information

Please use the following .bib entry to cite our paper:

@article{Soemers_2023_Transfer,
        author      = "D. J. N. J. Soemers and V. Mella and {\'E.} Piette and M. Stephenson and C. Browne and O. Teytaud",
        journal     = "Transactions on Machine Learning Research",
        title       = "Towards a General Transfer Approach for Policy-Value Networks",
        year        = "2023",
        url         = "\url{https://openreview.net/forum?id=vJcTm2v9Ku}"
}

See Also

See our other paper on "Deep Learning for General Game Playing with Ludii and Polygames" for more information on the bridge between Ludii and Polygames.

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Repo with scripts and documentation for the TMLR paper: "Towards a General Transfer Approach for Policy-Value Networks"

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