Parametric Transfer-Based DQN for Multi-Function Radar Jamming Decision Method
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
With the continuous development of multi-function radar technology, the number of radar tasks the seeker can perform is increasing. This has led to the environment state transitioning from a small space to an ample space, facing more complex radar jamming decision problems. Traditional reinforcement learning algorithms have insufficient processing capacity and limited learning ability, thus we adopted a deep reinforcement learning algorithm, combining its powerful perception and processing capabilities to improve the jamming effect further. At the same time, to solve the problem of low computational efficiency for deep reinforcement learning, the transfer learning algorithm is introduced by migrating the parameters of deep learning networks from other tasks to the radar seeker jamming decision, further improving the learning rate.
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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 | 0.000 |
| 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 it