Consensus Control of Micro Multi-Agent Reinforcement Learning Systems for Tumor Treatment
Bibliographic record
Abstract
A consensus control algorithm for micro multi-agent reinforcement learning (MMARL) systems, including a collision avoidance system (CAS) is presented in this paper to guarantee the safety of surrounding healthy tissues in the human body. Our proposed method employs multiple micro-agents to enhance treatment accuracy and efficiency in targeting and destroying cancer tumors within a complicated biological environment. Designing an MMARL system, developing a consensus control strategy considering CAS, and validating it using simulations focusing on potential in vivo applications have been presented here. The simulation results indicate that agent$A_{1}$showed the most consistent improvement, with 1.26% rewards increasing, while agent$A_{2}$displayed more variability, with 1.84% rewards raising. Moreover, agent$A_3$stabilized with 1.25% rewards boosting. Additionally, the agents displayed similar speed patterns, with agent$A_1$having the lowest variance in speed (∼ 0.1) and agent$A_{3}$the highest (∼ 0.2), reflecting differences in stability. The desired results contain increased targeting accuracy and minimized side effects, providing a non-invasive treatment rather than surgical procedures for high-risk patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".