Time-delayed Data Transmission in Heterogeneous Multi-agent Deep Reinforcement Learning System
Why this work is in the frame
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Bibliographic record
Abstract
This paper studies the data transmission between agents of a multi-agent, deep reinforcement learning (MADRL) system (leaderless and leader-follower) using the deep Q-network (DQN) algorithm. The structure of the MADRL system consists of various clusters of agents. The agents in a cluster have the same architectures. The DQN architecture is used to present the first cluster’s agents structure. The other clusters, including various architectures, are considered as the environment of the first cluster’s deep reinforcement learning (DRL) agent. The goal of each static agent is to transfer data with the maximum average reward. We consider two novel observations in data transmission termed on-time and time-delay. The two proposed observations are considered when the data transmission channel is idle and the data is transmitted on-time or time-delayed. Moreover, by considering the distance between the neighboring agents, we present a novel immediate reward function by appending a distance-based reward to the previously utilized reward. We have rigorously shown which system (on-time or time-delayed) has a superior performance based on the DQN loss and team reward for the entire team of agents. The claims have been proven theoretically, and the simulation confirms theoretical findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it