Leveraging Reinforcement Learning and WaveFunctionCollapse for Improved Procedural Level Generation
Bibliographic record
Abstract
This work presents a novel approach to training Reinforcement Learning (RL) agents to serve as heuristics for the WaveFunctionCollapse (WFC) algorithm in the production of procedurally generated video game levels. While the algorithm’s original minimal entropy heuristic is sufficient for constructing levels that look visually appealing, it is often the case that these levels suffer when playability is concerned. The approach presented in this work involves replacing this heuristic with a set of deep neural networks trained using RL to direct the algorithm in the construction of playable levels for the original Super Mario Bros. (SMB). We evaluate the performance of our models using a game-playing A*-based agent provided by the Mario AI Competition Framework and designate a simple reward function which reflects the quality of generated levels based on the A* agent’s ability to successfully navigate them. Results using this approach show an increase in the percentage of playable levels generated using our learned heuristics over those using minimal entropy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".