A Modified Reinforcement Q-Learning Method for Multi-function Phased Array Radar Beam Scheduling
Bibliographic record
Abstract
Modern multi-function radar is designed to perform a few functions such as guidance, fire control, communications, and surveillance. It needs schedule many tasks with different properties such as start time, dwell time, priority etc. In this type of radar, the radar resource management module makes decisions in task selection and task scheduling, which are NP-hard problems. Many task scheduling algorithms have been proposed, however it is still very challenging to choose the appropriate algorithm in varying environments. In this work, a modified Q-learning (QL) method, is developed to choose the optimal solution. The modified Q-learning (MQL) method is a reinforcement learning using the paradigm of Deep Q-Network. The MQL considers 8 states and 4 actions (scheduling algorithms) which are used to make decisions. The MQL agent is trained and tested for various episode limits ranging from 500 to 300,000. In each episode, tasks are generated randomly, for the training purpose. A cost function is formulated to compare scheduling performance. Our simulation results show that the proposed approach can choose the best algorithm consistently.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".