An investigation of methods of parameter tuning for Q-Learning Fuzzy Inference System
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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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".