A Survey of Applying Reinforcement Learning Techniques to Multicast Routing
Bibliographic record
Abstract
Multicast routing refers to the transmission of packets to a group of nodes identified by a single multicast group address. It plays a critical role in supporting applications that require group communication such as video conferencing and file distribution. One particularly challenging environment for multicast routing is Mobile Ad-hoc Networks (MANETs). The major problems facing routing in such networks are node mobility, frequently changing topology, unstable wireless links, and limited transmission range. Despite these challenges, several multicast applications like data base initialization and file distribution applications require reliable and efficient delivery of data. Various approaches have been proposed for MANET multicasting, but either suffer from low Packet Delivery Ratio (PDR) or high overhead and lack of scalability. In addition, reliable multicasting requires re-transmission of lost packets, which increases protocol overhead. Recently, Reinforcement Learning (RL) techniques have been successfully used in unicast routing to provide adaptive routing schemes. RL allows wireless nodes to make efficient routing decisions based on interaction with the environment while reducing the routing overhead compared to traditional routing approaches. In this paper, we investigate whether the same results hold when applying RL to multicasting in MANETs. We aim for two main performance criteria: ensuring 100% data delivery while at the same time reducing the total number of packets transmitted over the network. Based on these metrics, we evaluate existing RL-based multicast routing techniques and suggest promising approaches for reliable and efficient multicast routing protocols.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".