Reinforcement Learning in Super Mario Bros: Curriculum, Pedagogy, and Optimal Level Design in World 1-1
arXiv:2606.29511v1 Announce Type: new Abstract: World 1-1 of Super Mario Bros is widely celebrated as a masterclass in game design: its progressive structure is credited with teaching players core mechanics through the level itself. We ask whether that structure is empirically measurable using reinforcement learning. We implement World 1-1 from scratch as a fully discrete environment and compare four algorithms -- Q-Learning, SARSA, Monte Carlo, and Deep Q-Network (DQN) -- across three progressively complex versions of the same level. Monte Carlo emerges as the strongest agent (94.9% $\pm$ 1.5
The continuous advancements in reinforcement learning research and the increasing computational power make it feasible to apply sophisticated AI models to complex, long-standing problems in game theory and design.
This research provides empirical validation for established game design principles using AI, offering a template for optimal learning environments applicable beyond games to training AI agents in real-world scenarios.
The study provides measurable metrics for evaluating the 'pedagogy' of environments, transforming what was once intuitive design into data-driven optimization for AI training.
- · AI researchers
- · Game developers
- · AI education platforms
- · Intuitive-only game designers
It provides a quantifiable framework for evaluating the effectiveness of interactive environments in teaching AI.
This framework could lead to a new generation of 'pedagogically' designed AI training environments that accelerate model development.
The principles might extend to automated curriculum generation for human learning, optimizing educational content delivery based on AI-driven insights.
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Read at arXiv cs.LG