Carnegie Mellon, NYU, Stanford, MIT · 2026-10-01 · notable
Ataraxos — an $8,000 AI beats the best Stratego player 15-1
Ataraxos beat Stratego champion Pim Niemeijer 15-1 with four draws, per a Nature paper. Training took one week on 16 H100 GPUs, about $8,000. Code and weights are open under MIT.
A self-play AI masters Stratego, a game of hidden pieces, for about 1/500th of DeepNash's compute.
What is it?
Ataraxos is a game-playing AI from researchers at Carnegie Mellon, NYU, Stanford and MIT that beat Pim Niemeijer, the most decorated Stratego player in history, 15 wins to 1 loss with 4 draws over 20 games. The results are published in Nature, and the training code and pretrained weights are on GitHub under the MIT license.
How does it work?
The system learned from 163 million games of self-play with reinforcement learning. A separate belief model guesses the likely identity of the opponent's hidden pieces. At play time Ataraxos samples several possible hidden states, scores candidate moves against each one, and then commits to its best move.
Why does it matter?
Stratego hides most information and plays out over a very long game, which made it one of the last board games where humans still led. Ataraxos reached superhuman play on one week of 16 H100 GPUs, under $8,000, roughly 1/500th of what DeepMind's earlier DeepNash system used. The same methods also carried over to Barrage Stratego, Hanabi and dou dizhu.
Who is it for?
reinforcement-learning and game-AI researchers
Try it
git clone https://github.com/AtaraxosAI/stratego