# Student of Games: A unified learning algorithm for both perfect and imperfect information games

*Source* · `src-schmid-2023-student-of-games` · status: **n/a**

| field | value |
|---|---|
| kind | Peer-reviewed paper |
| venue | Science Advances 9(46) |
| date | 2023-11-17 |
| doi | 10.1126/sciadv.adg3256 |
| arxiv | 2112.03178 |
| accessed | 2026-10-24 |

## Authors

- Martin Schmid
- Matej Moravčík
- Neil Burch
- Rudolf Kadlec
- Josh Davidson
- Kevin Waugh
- Nolan Bard
- Finbarr Timbers
- Marc Lanctot
- G. Zacharias Holland
- Elnaz Davoodi
- Alden Christianson
- Michael Bowling

## Notes

Guided search, self-play learning, and game-theoretic reasoning in one sound algorithm; strong in chess and Go, beat the strongest open heads-up no-limit agent, and beat the prior best Scotland Yard agent.

## Backlinks (computed)

**cited by:** [Regret minimization and search for imperfect-information games](../technique/imperfect-information-solvers.md)

## Citation

Martin Schmid, Matej Moravčík, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, G. Zacharias Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling (2023-11-17). *Student of Games: A unified learning algorithm for both perfect and imperfect information games*. Science Advances 9(46) https://doi.org/10.1126/sciadv.adg3256

---
Provenance: created in run `2026-10-24-techniques-rl-design`.