MARL to Choose Actions On-the-Fly in a Cognitive Radar System
Bibliographic record
Abstract
Task scheduling for radar systems have generally taken a block approach, scheduling a given set of tasks, with predetermined task parameters, to optimize some cost function. This paper takes a contrasting, on-the-fly, approach, determining what the radar(s) must “do next” to maximize their understanding of their operating environment. Specifically, we develop reinforcement learning (RL) for joint tracking and surveillance in a multi-function radar system. We further develop a multi-agent reinforcement learning (MARL) system to coordinate the actions of two radars. In this paper, we define actions as the act of viewing an azimuth around a radar. We outline the system's architecture, environmental dynamics, RL agents employed, and comprehensive metrics for evaluating agents' performance. Our approach presents significant challenges due to the ambiguity in defining a state space, the extensive time required to train agents, and the large number of tunable RL hyperparameters. Additionally, the fundamentally opposed nature of tracking and searching tasks introduces a dichotomy in the requirements of a reward function for the RL agent. The findings of this paper provide insight and advancements in the field of cognitive radar systems.
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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 | 0.000 | 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".