# AI Learning and Conceptual Transfer in the Game of Hidden Rules

arXiv: https://arxiv.org/abs/2608.21372 · HTML: https://mlsanity.com/paper/2608.21372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 26 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 3 of 3)

- [The Game of Hidden Rules: A New Kind of Benchmark Challenge for Machine Learning](https://mlsanity.com/paper/2207.10218/) (2022)
- [Can We Distinguish Machine Learning from Human Learning?](https://mlsanity.com/paper/1910.03466/) (2019)
- [Toward a Metrology for Artificial Intelligence: Hidden-Rule Environments and Reinforcement Learning](https://mlsanity.com/paper/2509.06213/) (2025)

## Abstract (arXiv)

This report summarizes the work conducted on the Game of Hidden Rules (GOHR), focusing on reinforcement learning agents trained to infer hidden rules from trial-and-error feedback, representation design, rule difficulty analysis, transfer learning, generalization, and pseudo-bot-assisted human learning analysis. The report focuses on the Transformer-based A2C framework, Feature-Centric and Object-Centric representations, experimental findings, and classification of human learning data.