Deep Reinforcement Learning for URLLC in 5G Mission-Critical Cloud Robotic Application
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate the problem of robot swarm control in 5G mission-critical robotic applications, i.e., in an automated grid-based warehouse scenario. Such application requires both the kinematic energy consumption of the robots and the ultra-reliable and low latency communication (URLLC) between the central controller and the robot swarm to be jointly optimized in real-time. The problem is formulated as a nonconvex optimization problem since the achievable rate and decoding error probability with short block-length are neither convex nor concave in bandwidth and transmit power. We propose a deep reinforcement learning (DRL) based approach that employs the deep deterministic policy gradient (DDPG) method and convolutional neural network (CNN) to achieve a stationary optimal control policy that consists of a number of continuous and discrete actions. Numerical results show that our proposed multi-agent DDPG algorithm achieves a performance close to the optimal baseline and outperforms the single-agent DDPG in terms of decoding error probability and energy efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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