# Minimax Strikes Back

arXiv: https://arxiv.org/abs/2012.10700 · HTML: https://mlsanity.com/paper/2012.10700/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [Completeness of Unbounded Best-First Game Algorithms](https://mlsanity.com/paper/2109.09468/) (2021)
- [Spatial State-Action Features for General Games](https://mlsanity.com/paper/2201.06401/) (2022)
- [On some improvements to Unbounded Minimax](https://mlsanity.com/paper/2505.04525/) (2025)
- [Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge](https://mlsanity.com/paper/2302.04318/) (2023)
- [Deep Reinforcement Learning for 5*5 Multiplayer Go](https://mlsanity.com/paper/2405.14265/) (2024)
- [Enhancing Reinforcement Learning Through Guided Search](https://mlsanity.com/paper/2408.10113/) (2024)
- [AlphaBeta is not as good as you think: a simple class of synthetic games for a better analysis of deterministic game-solving algorithms](https://mlsanity.com/paper/2506.21996/) (2025)
- [Minibal: Balanced Game-Playing Without Opponent Modeling](https://mlsanity.com/paper/2603.23059/) (2026)
- [Completeness of Unbounded Best-First Minimax and Descent Minimax](https://mlsanity.com/paper/2603.24572/) (2026)
- [Study and Improvement of Search Algorithms in Multi-Player Perfect-Information Games](https://mlsanity.com/paper/2604.17378/) (2026)
- [The Surprising Effectiveness of Approximate Value Iteration in Self-Play](https://mlsanity.com/paper/2609.09094/) (2026)

## Abstract (arXiv)

Deep Reinforcement Learning reaches a superhuman level of play in many complete information games. The state of the art algorithm for learning with zero knowledge is AlphaZero. We take another approach, Ath'enan, which uses a different, Minimax-based, search algorithm called Descent, as well as different learning targets and that does not use a policy. We show that for multiple games it is much more efficient than the reimplementation of AlphaZero: Polygames. It is even competitive with Polygames when Polygames uses 100 times more GPU (at least for some games). One of the keys to the superior performance is that the cost of generating state data for training is approximately 296 times lower with Ath'enan. With the same reasonable ressources, Ath'enan without reinforcement heuristic is at least 7 times faster than Polygames and much more than 30 times faster with reinforcement heuristic.