Buckets:
| # BenchMAX policy store | |
| **October 2026 Early Preview — Official Release Planned for January 2027** | |
| Reference policy collection for [BenchMAX](https://github.com/ALPIuzh/benchmax-preview), a library for training and evaluating multi-agent policies. | |
| ## Connect | |
| [Install BenchMAX](https://github.com/ALPIuzh/benchmax-preview#readme) with its `training` extra to load neural policies. | |
| ## Browse | |
| ```python | |
| from benchmax.storage import reference_store | |
| store = reference_store() | |
| for ref in store.find_policies(): | |
| training = ref.training | |
| print(training.game.name, training.algorithm.name, training.seed) | |
| ``` | |
| Discovery includes all configurations when no game or algorithm is supplied. When filtering by name, use `training_config="any"` or `game_config="any"` to include nondefault configurations. | |
| Some examples via the CLI: | |
| ```sh | |
| benchmax store info | |
| benchmax store show --game kuhn_poker --algorithm mappo | |
| benchmax store show --json | |
| ``` | |
| ## Evaluate or export | |
| This example selects the current rock-paper-scissors MAPPO policy. Adjust the algorithm and seed using the listing above. | |
| ```python | |
| from benchmax import load_game | |
| game = load_game("rock_paper_scissors") | |
| reference, = store.find_policies( | |
| game, algorithm="mappo", seed=2009, training_config="any" | |
| ) # requires exactly one matching artifact | |
| metrics = game.evaluate(reference) | |
| local = reference.save(path="saved-policy") | |
| ``` | |
| Find more advanced usage and information in the official [storage guide](https://github.com/ALPIuzh/benchmax-preview/blob/main/src/benchmax/storage/README.md). | |
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