Adversarial Attacks on Heterogeneous Multi-Agent Deep Reinforcement Learning System with Time-Delayed Data Transmission
Bibliographic record
Abstract
This paper studies the gradient-based adversarial attacks on cluster-based, heterogeneous, multi-agent, deep reinforcement learning (MADRL) systems with time-delayed data transmission. The structure of the MADRL system consists of various clusters of agents. The deep Q-network (DQN) architecture presents the first cluster’s agent structure. The other clusters are considered as the environment of the first cluster’s DQN agent. We introduce two novel observations in data transmission, termed on-time and time-delay observations. The proposed observations are considered when the data transmission channel is idle, and the data is transmitted on time or delayed. 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 to improve the MADRL system performance. We consider three types of gradient-based attacks to investigate the robustness of the proposed system data transmission. Two defense methods are proposed to reduce the effects of the discussed malicious attacks. We have rigorously shown the system performance based on the DQN loss and the team reward for the entire team of agents. Moreover, the effects of the various attacks before and after using defense algorithms are demonstrated. The theoretical results are illustrated and verified with simulation examples.
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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 | 0.003 |
| 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 | 0.001 |
| 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".