# Multiplayer AlphaZero

arXiv: https://arxiv.org/abs/1910.13012 · HTML: https://mlsanity.com/paper/1910.13012/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for General Game Playing with Ludii and Polygames](https://mlsanity.com/paper/2101.09562/) (2021)
- [AlphaDDA: Strategies for Adjusting the Playing Strength of a Fully Trained AlphaZero System to a Suitable Human Training Partner](https://mlsanity.com/paper/2111.06266/) (2021)
- [Completeness of Unbounded Best-First Game Algorithms](https://mlsanity.com/paper/2109.09468/) (2021)
- [Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman](https://mlsanity.com/paper/2305.13206/) (2023)
- [Deep Reinforcement Learning for 5*5 Multiplayer Go](https://mlsanity.com/paper/2405.14265/) (2024)
- [AlphaViT: A flexible game-playing AI for multiple games and variable board sizes](https://mlsanity.com/paper/2408.13871/) (2024)

## Abstract (arXiv)

The AlphaZero algorithm has achieved superhuman performance in two-player, deterministic, zero-sum games where perfect information of the game state is available. This success has been demonstrated in Chess, Shogi, and Go where learning occurs solely through self-play. Many real-world applications (e.g., equity trading) require the consideration of a multiplayer environment. In this work, we suggest novel modifications of the AlphaZero algorithm to support multiplayer environments, and evaluate the approach in two simple 3-player games. Our experiments show that multiplayer AlphaZero learns successfully and consistently outperforms a competing approach: Monte Carlo tree search. These results suggest that our modified AlphaZero can learn effective strategies in multiplayer game scenarios. Our work supports the use of AlphaZero in multiplayer games and suggests future research for more complex environments.