Model-Based DRL for Task Scheduling in Dynamic Environments for Cognitive Multifunction Radar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The uncertainty in the radar environment brings significant challenges to task scheduling in a cognitive multifunction radar (MFR). The recent radar task scheduling approaches assume the knowledge of the environment, which is unknown in real scenarios. To achieve online task scheduling for a cognitive radar which does not need to know the dynamics of the environment, this work investigates the model-based deep reinforcement learning (DRL), which learns the model of the environment to plan for scheduling tasks. The main idea of the model-based methods is to construct an abstract Markov-decision process (MDP) model such that planning in the abstract MDP is equivalent to planning in the real environment. Here, we tailor MuZero, proposed to learn to play games, to provide the needed model. The proposed algorithm is shown to be effective in MFR task scheduling while adapting to dynamic radar environments, without any a priori knowledge.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it