Goal-Oriented Reinforcement Learning in THz-Enabled UAV-Aided Network Using Supervised Learning
Bibliographic record
Abstract
Deep reinforcement learning (DRL) has been a key machine learning technique in many 5G and 6G applications. DRL agents learn optimal (or sub-optimal) policies by interacting with the environment. However, this process often involves numerous uninformative and repetitive message transmissions between the DRL agent and its environment. In this paper, we address the problem of reducing interactions between the DRL agent and the environment, called goal-oriented DRL. Meanwhile, Terahertz (THz) bands and unmanned aerial vehicles (UAVs) are considered two of the main enablers of 6G. Therefore, we investigate the goal-oriented DRL problem in a THz-enabled UAV-aided network. We formulate it as an optimization problem with the goals of i) reducing interactions between the UAV (DRL agent) and IoT devices (environment), ii) maximizing the number of served IoT devices, and iii) ensuring fairness. The constraints include the movement characteristics of IoT devices, the maximum speed limitation of the UAV, the QoS requirements of the served IoT devices, and the limited uplink coverage of the THz-enabled UAV. This problem is a mixed-integer nonlinear programming optimization problem and is NP-hard. To address this problem, we employ the decoupling optimization method and an approach inspired by the self-triggered method from control engineering. Specifically, the problem is divided into two sub-problems; Then, we propose using supervised learning as a teacher for DRL to reduce the interactions. Our simulation results show that the goal-oriented DRL approach outperforms conventional methods by reducing interactions and maintaining good performance in terms of the number of served IoT devices and fairness.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".