A fuzzy reinforcement learning algorithm using a predictor for pursuit-evasion games
Bibliographic record
Abstract
In a pursuit-evasion game, the pursuer learning its strategy by any learning algorithm usually captures the evader when the environment of the game is similar to the environment that the pursuer was trained on. However, the trained pursuer may not be able to capture the evader if the environment of the pursuit-evasion game is different from the training environment. In this paper, we propose a fuzzy reinforcement learning algorithm so that the ability of the pursuer to capture the evader, in a pursuit-evasion game, will increase even when the environment of the game is different from the training environment. The proposed algorithm predicts the future position of the evader using a Kalman filter and then tunes the fuzzy logic controller (FLC) of the pursuer so that the pursuer moves directly to the expected position of the evader, where the capture of the evader will occur. The proposed algorithm is called the Kalman filter fuzzy actor critic learning (KFFACL) algorithm. The proposed KFFACL algorithm is applied to pursuitevasion games that have environments different from the training environment. Simulation results show that the proposed KFFACL algorithm outperforms the state-of-the-art fuzzy reinforcement learning algorithms in terms of the ability of the pursuer to capture the evader and the capture time.
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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) | 0.002 | 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".