Deep Reinforcement Learning for Latency-Sensitive Communication With Adaptive Redundant Retransmissions
Bibliographic record
Abstract
This paper studies packet repetition strategies over erasure channels with memory and a long feedback delay. The problem is initially formulated as a communications problem where a source wishes to transmit one message packet to a destination while minimizing both the delay and the number of transmissions. At each time instant, the sender is provided a delayed acknowledgement feedback about past attempts, and must decide whether to attempt a new transmission or not. This problem is then re-formulated as an episodic reinforcement learning problem, where an agent attempts to learn the optimal transmission policy, provided delayed feedback about past transmission attempts. The agent is helped by a channel estimator, which attempts to capture the channel memory and use that to predict probabilities of erasures in a future window. This channel estimator is also data-driven and learns the channel model without anya priorichannel knowledge. The paper presents a lower bound on the achievable trade-off between delay and number of transmissions for any channel modeled as a Markov process. Experimental results show that the combination of the proposed channel estimator and the agent can noticeably outperform naive strategies for channels with memory, and achieves results close to the lower bound.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".