# Deep Reinforcement Learning for 5*5 Multiplayer Go

arXiv: https://arxiv.org/abs/2405.14265 · HTML: https://mlsanity.com/paper/2405.14265/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 4 of 4)

- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Learning to Play Two-Player Perfect-Information Games without Knowledge](https://mlsanity.com/paper/2008.01188/) (2020)
- [Minimax Strikes Back](https://mlsanity.com/paper/2012.10700/) (2020)
- [Multiplayer AlphaZero](https://mlsanity.com/paper/1910.13012/) (2019)

## 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.