On the influence of learning time on evolutionary online learning of cooperative behavior
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Bibliographic record
Abstract
We present an online learning approach for learning cooperative behavior in multi-agent systems based on invoking an offline learning method as a special action learn. We apply this approach to evolutionary offline learning using situation-action-pairs and the nearest-neighbor rule as agent architecture. For the application Pursuit Games we show that the online approach using evolutionary offline learning allows for good success rates for rather different game variants. Particularly, we perform experiments highlighting the influence of the time needed for learning and of the parameters of the evolutionary offline method. Our results show that even a duration of learn which is several times longer than the usual duration of an agent's actions still achieves good success rates. The same applies to rather small values for the key parameters of the offline method. Together, this suggests that this evolutionary online learning approach is a very good alternative to the well-known online approaches based on reinforcement learning.
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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 it