Finite Automata Extraction: Low-data World Model Learning as Programs from Gameplay Video

Dave Goel; Matthew Guzdial; Anurag Sarkar

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arXiv · 2508.11836

Finite Automata Extraction: Low-data World Model Learning as Programs from Gameplay Video

Dave Goel·Matthew Guzdial·Anurag Sarkar

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Abstract

World models are defined as a compressed spatial and temporal learned representation of an environment. The learned representation is typically a neural network, making transfer of the learned environment dynamics and explainability a challenge. In this paper, we propose an approach, Finite Automata Extraction (FAE), that learns a neuro-symbolic world model from gameplay video represented as programs in a novel domain-specific language (DSL): Retro Coder. Compared to prior world model approaches, FAE learns a more precise model of the environment and more general code than prior DSL-based approaches.

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Dave Goel, Matthew Guzdial, Anurag Sarkar. 2025-08-15. Finite Automata Extraction: Low-data World Model Learning as Programs from Gameplay Video. https://arxiv.org/abs/2508.11836

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