Toward a Metrology for Artificial Intelligence: Hidden-Rule Environments and Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912665535774720 |
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| author | Mathew, Christo Wang, Wentian Feldman, Jacob Gallos, Lazaros K. Kantor, Paul B. Menkov, Vladimir Wang, Hao |
| author_facet | Mathew, Christo Wang, Wentian Feldman, Jacob Gallos, Lazaros K. Kantor, Paul B. Menkov, Vladimir Wang, Hao |
| contents | 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$6 board by placing game pieces into buckets. We explore two state representation strategies, namely Feature-Centric (FC) and Object-Centric (OC), and employ a Transformer-based Advantage Actor-Critic (A2C) algorithm for training. The agent has access only to partial observations and must simultaneously infer the governing rule and learn the optimal policy through experience. We evaluate our models across multiple rule-based and trial-list-based experimental setups, analyzing transfer effects and the impact of representation on learning efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward a Metrology for Artificial Intelligence: Hidden-Rule Environments and Reinforcement Learning Mathew, Christo Wang, Wentian Feldman, Jacob Gallos, Lazaros K. Kantor, Paul B. Menkov, Vladimir Wang, Hao Machine Learning Artificial Intelligence We investigate reinforcement learning in the Game Of Hidden Rules (GOHR) environment, a complex puzzle in which an agent must infer and execute hidden rules to clear a 6$\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. |
| title | Toward a Metrology for Artificial Intelligence: Hidden-Rule Environments and Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.06213 |