Discovering Agent Behaviors Through Code Reuse: Examples From Half-Field Offense and Ms. Pac-Man
Bibliographic record
Abstract
This paper demonstrates how code reuse allows genetic programming (GP) to discover strategies for difficult gaming scenarios while maintaining relatively low model complexity. Critical factors in the proposed approach are illustrated through an in-depth study in two challenging task domains: RoboCup soccer and Ms. Pac-Man. In RoboCup, we demonstrate how policies initially evolved for simple subtasks can be reused, with no additional training or transfer function, in order to improve learning in the complex half-field offense (HFO) task. We then show how the same approach to code reuse can be applied directly in Ms. Pac-Man. In the latter case, the use of task-agnostic diversity maintenance removes the need to explicitly identify suitable subtasks a priori. The resulting GP policies achieve state-of-the-art levels of play in HFO and surpass scores previously reported in the Ms. Pac-Man literature, while employing less domain knowledge during training. Moreover, the highly modular policies discovered by GP are shown to be significantly less complex than state-of-the-art solutions in both domains. Throughout this paper, we pay special attention to a pair of task-agnostic diversity maintenance techniques, and empirically demonstrate their importance to the development of strong policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".