Multi-Invader Multi-Defender Differential Game Using Reinforcement Learning
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Bibliographic record
Abstract
This paper addresses a game of guarding a territory with several invaders and several defenders. Although there are a plethora of studies for the single-invader single-defender scenario, or single-invader multi-defender case, there are few articles on the multi-invader case. The reason is the assignment problem. Each defender must know the policy for capturing each invader. In addition, each defender must choose an invader to capture during the game. We proposed a hierarchical reinforcement learning (HRL) process to solve the assignment problem in a multi-invader multi-defender game of guarding a territory. In the proposed method, a higher-level policy is able to change the assignment in the middle of the game as the game environment is changing. The proposed method is also examined on the game of active target defence. It is shown that the proposed method can solve the assignment problem without any knowledge of an external optimal policy, such as geometric approaches.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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