{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T03:47:23Z","timestamp":1777693643303,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICG"],"published-print":{"date-parts":[[2022,2,21]]},"abstract":"<jats:p>Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game\u2019s rules, and constructing the neural network\u2019s architecture \u2013 in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 1,000 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.<\/jats:p>","DOI":"10.3233\/icg-220197","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T12:59:50Z","timestamp":1644929990000},"page":"146-161","source":"Crossref","is-referenced-by-count":6,"title":["Deep learning for general game playing with Ludii and Polygames"],"prefix":"10.1177","volume":"43","author":[{"given":"Dennis J.N.J.","family":"Soemers","sequence":"first","affiliation":[{"name":"Department of Data Science and Knowledge Engineering, Maastricht University, The Netherlands"},{"name":"Meta AI Research, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vegard","family":"Mella","sequence":"additional","affiliation":[{"name":"Meta AI Research, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cameron","family":"Browne","sequence":"additional","affiliation":[{"name":"Department of Data Science and Knowledge Engineering, Maastricht University, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Teytaud","sequence":"additional","affiliation":[{"name":"Meta AI Research, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/ICG-220197_ref1","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1613\/jair.3912","article-title":"The Arcade Learning Environment: An Evaluation Platform for General Agents","volume":"47","author":"Bellemare","year":"2013","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10.3233\/ICG-220197_ref2","unstructured":"Brown, N., Bakhtin, A., Lerer, A. & Gong, Q. (2020). Combining deep reinforcement learning and search for imperfect-information games. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan and H. Lin (Eds.), Advances in Neural Information Processing Systems 33 (NeurIPS 2020)."},{"issue":"1","key":"10.3233\/ICG-220197_ref3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCIAIG.2012.2186810","article-title":"A survey of Monte Carlo tree search methods","volume":"4","author":"Browne","year":"2012","journal-title":"IEEE Transactions on Computational Intelligence and AI in Games"},{"key":"10.3233\/ICG-220197_ref4","doi-asserted-by":"crossref","unstructured":"Browne, C., Stephenson, M., Piette, \u00c9. & Soemers, D.J.N.J. (2020). A practical introduction to the Ludii general game system. In T. Cazenave, J. van den Herik, A. Saffidine and I.-C. Wu (Eds.), Advances in Computer Games. ACG 2019. Lecture Notes in Computer Science (LNCS). 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Ludii \u2013 the ludemic general game system. In G.D. Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugar\u00edn and J. Lang (Eds.), Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020). Frontiers in Artificial Intelligence and Applications (Vol. 325, pp. 411\u2013418). IOS Press."},{"key":"10.3233\/ICG-220197_ref23","unstructured":"Pitrat, J. (1968). Realization of a general game-playing program. In A.J.H. Morrel (Ed.), Information Processing. Proceedings of IFIP Congress 1968 Edinburgh, UK, 5\u201310 August 1968 (Vol. 2, pp. 1570\u20131574). 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