The first scientific contribution to this issue is Deep Learning for General Game Playing with Ludii and Polygames by Dennis J. N. J. Soemers, Vegard Mella, Cameron Browne and Olivier Teytaud. Polygames is a general Deep Reinforcement Learning engine that combines Monte Carlo Tree Search and Deep Neural Networks trained through self-play. Ludii is a general game system that now contains more than one thousand games. The paper describes the bridge between Ludii and Polygames that enables Polygames to train and evaluate games that are implemented and run through Ludii. Polygames can represent any game implemented in Ludii. The authors also give experimental results for multiple board games and discuss future research.

中文翻译：

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社论：Ludii、Konane 和计算机奥林匹克

对此问题的第一个科学贡献是 Dennis JNJ Soemers、Vegard Mella、Cameron Browne 和 Olivier Teytaud 的 Ludii 和 Polygames 的通用游戏深度学习。Polygames 是一个通用的深度强化学习引擎，它结合了蒙特卡洛树搜索和通过自我游戏训练的深度神经网络。Ludii 是一个通用的游戏系统，现在包含一千多个游戏。该论文描述了 Ludii 和 Polygames 之间的桥梁，使 Polygames 能够训练和评估通过 Ludii 实现和运行的游戏。Polygames 可以代表在 Ludii 中实现的任何游戏。作者还给出了多个棋盘游戏的实验结果并讨论了未来的研究。