Game-Agnostic Value Functions through Automatic JSON Feature Extraction

Abstract:JSON Bag-of-Tokens (JSON-Bag) is a recently proposed method to generically represent game trajectories by tokenizing their JSON descriptions. We introduce JSON-Bag VF, a game-agnostic approach to training value functions for game-playing agents using JSON-Bag prototypes. We show that this approach can be enhanced with Random Forest-based feature selection and a method to select game-stage-specific features. We evaluate JSON-Bag VF with One-step-look-ahead (JSON-Bag OSLA) on six tabletop games over different combinations of prototype-tokenization and feature selections. JSON-Bag OSLA outperforms baseline OSLA agents in most games. Our analysis also shows that feature selection significantly improves JSON-Bag VF and that feature selection is the most important factor in JSON-Bag VF performance, over prototype-tokenization.

Artificial Intelligence (cs.AI)

arXiv:2608.30056 [cs.AI]

(or arXiv:2608.30056v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.30056 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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