Mixed-Reward Multiagent Proximal Policy Optimization Method for Two-on-Two Beyond-Visual-Range Air Combat
Bibliographic record
Abstract
With recent advances in airborne weapons, modern air combats tend to be accomplished in the beyond-visual-range (BVR) phase. Multiaircraft cooperation is also required to adapt to the complexities of modern air combats. The scale of the traditional rule-based expert system will become incredible in this case. In view of this, a mixed-reward multiagent proximal policy optimization (MRMAPPO) method is proposed in this article that is used to help train cooperative BVR air combat tactics via adversarial self-play. First, a two-on-two BVR air combat simulation platform is established, and the combat game is modeled as a Markov game. Second, centralized training with decentralized execution architecture is established. Multiple actors are involved in the architecture, each corresponding to a policy that generates a specified kind of command, e.g., the maneuvering and firing command. Moreover, in order to accelerate training as well as enhance the stability of the training process, four optimization mechanisms are introduced. The experimental section discusses how the effectiveness of the MRMAPPO is verified with comparative and ablation experiments, along with several air combat tactics that emerge in the training process.
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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 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".