Learning in n-pursuer n-evader differential games
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Bibliographic record
Abstract
This paper discusses learning in n-purser n-evader games. In a pursuit-evasion game, one player (the pursuer) pursues another one while the other (the evader) tries to escape. We assume that each player only knows the instantaneous position of the other players but at the same time none of them knows its control strategy nor the control strategy of the other players. Therefore, the players have to self-learn their control strategies on-line by interaction with each other. In this paper, we extend our previous work from learning in a single pursuit-evasion game to learning in a multi-pursuit-evasion game. We use the Q(λ)-learning based genetic fuzzy controller technique (QLBGFC) proposed in. The proposed technique combines reinforcement learning with both a fuzzy controller and genetic algorithms in a two-phase structure. In addition to the proposed QLBGFC, we construct a new Q-table that is responsible for learning the coupling process between the pursuers and the evaders. To test the performance of the proposed technique, it is compared with the optimal strategy of a single pursuit-evasion game. Computer simulations show the usefulness of the proposed technique.
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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