Multi-Agent Reinforcement Learning for Resilient Channel Access in Smart Grid Networks Under Intelligent Adversarial Interference
Bibliographic record
Abstract
Harnessing the potential of smart grid networks relies on the efficient and secure communication between distributed energy resources (DERs) and the energy management system (EMS), particularly under variable channel conditions and adversarial interference. This interference is further exacerbated with the advent of artificial intelligence (AI)-driven adversarial devices capable of adaptively disrupting communication in real time. To address these challenges, in this study, we formulate the distributed channel access problem, incorporating dynamic channels and intelligent adversarial interference as a partially observable Markov game (POMG). Specifically, we propose a distributed framework based on a centralized training and distributed execution (CTDE) multi-agent reinforcement learning (MARL), enabling DERs to autonomously adapt to dynamic channels and mitigate intelligent interference using only local observations, i.e., without direct information sharing among DERs. Our simulation results indicate that, by integrating an advanced policy evaluation technique and a tailored utility maximization strategy, DERs can collaboratively optimize their transmission decisions, improving the network's aggregate packet success rate (APSR) and resilience. Additionally, the proposed framework outperforms existing methods, ensuring robust and scalable communication in smart grids under diverse conditions.
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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".