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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{1}$</tex> showed the most consistent improvement, with 1.26% rewards increasing, while agent <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{2}$</tex> displayed more variability, with 1.84% rewards raising. Moreover, agent <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_3$</tex> stabilized with 1.25% rewards boosting. Additionally, the agents displayed similar speed patterns, with agent <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_1$</tex> having the lowest variance in speed (∼ 0.1) and agent <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{3}$</tex> 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 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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".