A fuzzy reinforcement learning algorithm with a prediction mechanism
Bibliographic record
Abstract
This paper applies fuzzy reinforcement learning along with state estimation to the differential pursuit-evasion game. The proposed algorithm is a modified version of the Q(λ) Learning Fuzzy Inference System (QLFIS) algorithm proposed in [10]. The proposed algorithm combines the QLFIS algorithm with a Kalman filter estimation approach. The proposed algorithm is called the modified Q(λ)-learning fuzzy inference system (MQLFIS) algorithm. The Kalman filter is used by the pursuer to estimate the expected future position of the evader. The proposed algorithm tunes the input and the output parameters of the fuzzy logic controller (FLC) of the pursuer based on the expected future position of the evader instead of the real position of the evader. The proposed algorithm also uses the expected future position of the evader to generate the output of the FLC so that the pursuer captures the evader at the expected future position. The proposed algorithm is used to learn two different single pursuit-evasion games. Simulation results show that the performance of the proposed MQLFIS algorithm outperforms the performance of the QLFIS algorithm proposed in [10].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".