Deep Reinforcement Learning for 5*5 Multiplayer Go

• URL: http://arxiv.org/abs/2405.14265v1
• Date: Thu, 23 May 2024 07:44:24 GMT
• Title: Deep Reinforcement Learning for 5*5 Multiplayer Go
• Authors: Brahim Driss, Jérôme Arjonilla, Hui Wang, Abdallah Saffidine, Tristan Cazenave,
• Abstract summary: We propose to use and analyze the latest algorithms that use search and Deep Reinforcement Learning (DRL) We show that using search and DRL we were able to improve the level of play, even though there are more than two players.
• Score: 6.222520876209623
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In recent years, much progress has been made in computer Go and most of the results have been obtained thanks to search algorithms (Monte Carlo Tree Search) and Deep Reinforcement Learning (DRL). In this paper, we propose to use and analyze the latest algorithms that use search and DRL (AlphaZero and Descent algorithms) to automatically learn to play an extended version of the game of Go with more than two players. We show that using search and DRL we were able to improve the level of play, even though there are more than two players.

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