Deep Reinforcement Learning for General Game Playing

Autor:	Adrian Goldwaser, Michael Thielscher
Rok vydání:	2020
Předmět:	Computer science Human–computer interaction ComputingMilieux_PERSONALCOMPUTING Reinforcement learning General Medicine Heuristics computer.software_genre computer General game playing
Zdroj:	AAAI
ISSN:	2374-3468 2159-5399
DOI:	10.1609/aaai.v34i02.5533
Popis:	General Game Playing agents are required to play games they have never seen before simply by looking at a formal description of the rules of the game at runtime. Previous successful agents have been based on search with generic heuristics, with almost no work done into using machine learning. Recent advances in deep reinforcement learning have shown it to be successful in some two-player zero-sum board games such as Chess and Go. This work applies deep reinforcement learning to General Game Playing, extending the AlphaZero algorithm and finds that it can provide competitive results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::565a7f5e458f13b29fe8421964286a5b https://doi.org/10.1609/aaai.v34i02.5533 Zobrazit plný text záznamu