Decentralized learning in pursuit-evasion differential games with multi-pursuer and single-superior evader
Bibliographic record
Abstract
In this paper, we consider a multi-pursuer single-superior-evader pursuit-evasion differential game where the speed of the evader is similar to the speed of each pursuer. A new fuzzy reinforcement learning algorithm is proposed in this work for this game. Each pursuer of the game uses the proposed algorithm to learn its control strategy. The proposed algorithm of each pursuer uses the residual gradient fuzzy actor critic learning (RGFACL) algorithm to tune the parameters of the fuzzy logic controller (FLC) of the pursuer. The proposed algorithm uses a formation control approach in the tuning mechanism of the FLC of the learning pursuer so that the learning pursuer or the other learning pursuers can capture the superior evader. The formation control mechanism used by the proposed algorithm guarantees that the pursuers are distributed around the superior evader in order to avoid collision between pursuers. The formation control mechanism also guarantees that the capture regions of each two adjacent pursuers overlap or at least border each other so that the capture of the superior evader will be guaranteed. The proposed algorithm is a decentralized algorithm as no communication among pursuers is required. The only information that the proposed algorithm of each learning pursuer requires is the position and the speed of the superior evader. The proposed algorithm is used to learn a multi-pursuer single-superior-evader pursuitevasion differential game. The simulation results show the effectiveness of the proposed algorithm as the superior evader is always captured by one or some of the pursuers learning their strategies by the proposed algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".