Cooperative control for multi-player pursuit-evasion games embedded on communication technology with reinforcement learning
Bibliographic record
Abstract
Abstract Recent advances in research on the Multi-agent System (MAS) optimal control issue will help sectors like robotics, communications, and power systems. This work looks at the intelligent design of a large-scale multi-pursuer and multi-evader pursuit-evasion game. Based on reinforcement learning, a distributed cooperative pursuit method with communication is created. The famed Curse of Dimensionality poses a serious danger to multi-player pursuit-evasion game designs due to the sheer number of agents, especially in hostile areas where there aren't many communication options available to encourage player information exchange. In order to find the best pursuit-evasion strategies using a novel type of probability density function (PDF) rather than exhaustive data from all the remaining teams or agents, the Mean Field Games (MFG) theory has been used. A novel MAS optimum type oversight system with a decentralised and computer-friendly decision method is urgently needed. Mean field game theory is used to create the Actor-critic-mass (ACM), a decentralised optimal control system, to address the aforementioned issues. Additionally, the homogeneous decentralised Actor-critic-mass (HDACM) which improves the ACM method, does away with restrictions like homogeneous agents and cost functions. Finally, two applications make use of the PAS 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 | 0.000 | 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".