An investigation of methods of parameter tuning for Q-Learning Fuzzy Inference System
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Bibliographic record
Abstract
This paper investigates four methods of implementing a Q-Learning Fuzzy Inference System (QFIS) algorithm to autonomously tune the parameters of a fuzzy inference system. We use an actor-critique structure and we simulate mobile robots playing the differential form of the pursuit evasion game. Both the critique and the actor are fuzzy inference systems. The four methods come from the fact whether it is necessary to tune all the parameters (i.e. all the premise and the consequent parameters) of the critique and the actor or just tune their consequent parameters. The four methods are applied to three versions of the pursuit evasion games. In the first version just the pursuer is learning. In the second version, the evader uses its higher maneuverability and plays intelligently against a self-learning pursuer. In the final version, both the pursuer and the evader are learning. We evaluate which parameters are best to tune and which parameters have little impact on the performance.
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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 | 0.001 | 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