Deep Reinforcement Learning for Network Provisioning in Elastic Optical Networks
Bibliographic record
Abstract
We design an effective and scalable Deep Reinforcement Learning (DRL) approach for the Routing, Modulation and Spectrum Assignment (RMSA) problem in elastic optical networks. We use Convolutional Neural Networks (CNN) to embed the state and Deep Neural Networks (DNN) to learn the policy. We propose a novel state representation and reward function that interestingly guide the agent on assigning appropriate routes and spectrum by incorporating information on the spectrum utilisation and spectrum fragmentation. This gives the agent information about the consequence or cost of each action across the network, reducing the level of knowledge abstraction required for the agent. To show the effectiveness of the reward function and the importance of well-designed state representations, we have designed two state representations: the first with aggregation of spectrum occupancy information and the second without aggregation. The Proximal Policy Optimization (PPO) algorithm is investigated with an actor critic model where an entropy bonus is added to the loss function to ensure sufficient exploration. The proposed solution is compared with a greedy heuristic and a PPO with standard reward and state representation. Numerical results show that the proposed model provides very good solutions and works well on dataset instances with large topologies (up to 75 nodes). The proposed PPO outperformed the baseline algorithms by obtaining the largest throughput on all test instances. In addition, its spectrum usage has the lowest fragmentation.
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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) | 0.002 | 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".