Deep Reinforcement Learning for 5*5 Multiplayer Go
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 19 references that are in the map. First posted on arXiv 23 May 2024.
Builds on
4 of 19 references.
References · 4
- 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
- 2020Learning to Play Two-Player Perfect-Information Games without Knowledgecited by 11
- 2020Minimax Strikes Backcited by 11
- 2019Multiplayer AlphaZerocited by 6
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- 2025Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations
- 2026Mastering the Game of Go with Self-play Experience Replay
- 2025A Practical Introduction to Deep Reinforcement Learning
- 2025Deep Reinforcement Learning Xiangqi Player with Monte Carlo Tree Search
- 2024Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm
- 2026Learning to Play: Reinforcement Learning and Games
- 2025Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
- 2024A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges