Toward a Metrology for Artificial Intelligence: Hidden-Rule Environments and Reinforcement Learning

• URL: http://arxiv.org/abs/2509.06213v3
• Date: Thu, 23 Oct 2025 04:14:39 GMT
• Title: Toward a Metrology for Artificial Intelligence: Hidden-Rule Environments and Reinforcement Learning
• Abstract summary: We investigate reinforcement learning in the Game Of Hidden Rules (GOHR) environment.<n>GOHR is a complex puzzle in which an agent must infer and execute hidden rules to clear a 6$times$6 board.<n>We employ two state representation strategies, namely Feature-Centric (FC) and Object-Centric (OC), and employ a Transformer-based Advantage Actor-Critic (A2C) algorithm for training.
• Score: 2.614526609561279
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate reinforcement learning in the Game Of Hidden Rules (GOHR) environment, a complex puzzle in which an agent must infer and execute hidden rules to clear a 6$\times$6 board by placing game pieces into buckets. We explore two state representation strategies, namely Feature-Centric (FC) and Object-Centric (OC), and employ a Transformer-based Advantage Actor-Critic (A2C) algorithm for training. The agent has access only to partial observations and must simultaneously infer the governing rule and learn the optimal policy through experience. We evaluate our models across multiple rule-based and trial-list-based experimental setups, analyzing transfer effects and the impact of representation on learning efficiency.

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