Task Scheduling in Cognitive Multifunction Radar Using Model-Based DRL
Bibliographic record
Abstract
There has been much recent work on machine learning-based approaches for cognitive task scheduling in multifunction radar (MFR). However, the available MFR scheduling approaches rely on knowledge of the operating environment; in practice, though, the inherent uncertainty with dynamic radar environments poses significant challenges, especially for cognitive MFR. Here, we address the need for online task scheduling in a cognitive MFR without prior knowledge of the environment. We draw inspiration from recent advancements in model-based deep reinforcement learning, specifically the applicability of MuZero, to enable cognitive MFR in unknown and continuously changing environments. In this approach, the scheduler learns an abstract Markov decision process (MDP) model of the environment, allowing near-optimal abstract MDP planning to translate effectively into the actual environment. However, with exponential complexity, the published MuZero approach requires long training times and is useful only for a few tasks. Here, we modify the original MuZero algorithm to accommodate large number of tasks by incorporating prior knowledge of the task scheduling problem. Our numerical results show that the modified-MuZero approach is effective and computationally efficient in complex radar scenarios.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".