Reward shaping in DRL: A novel framework for adaptive resource management in dynamic environments
Bibliographic record
Abstract
In edge computing environments, efficient computation resource management is crucial for optimizing service allocation to hosts in the form of containers. These environments experience dynamic user demands and high mobility, making traditional static and heuristic-based methods inadequate for handling such complexity and variability. Deep Reinforcement Learning (DRL) offers a more adaptable solution, capable of responding to these dynamic conditions. However, existing DRL methods face challenges such as high reward variability, slow convergence, and difficulties in incorporating user mobility and rapidly changing environmental configurations. To overcome these challenges, we propose a novel DRL framework for computation resource optimization at the edge layer. This framework leverages a customized Markov Decision Process (MDP) and Proximal Policy Optimization (PPO), integrating a Graph Convolutional Transformer (GCT). By combining Graph Convolutional Networks (GCN) with Transformer encoders, the GCT introduces a spatio-temporal reward-shaping mechanism that enhances the agent's ability to select hosts and assign services efficiently in real time while minimizing the overload. Our approach significantly enhances the speed and accuracy of resource allocation, achieving, on average across two datasets, a 30% reduction in convergence time, a 25% increase in total accumulated rewards, and a 35% improvement in service allocation efficiency compared to standard DRL methods and existing reward-shaping techniques. Our method was validated using two real-world datasets, MOBILE DATA CHALLENGE (MDC) and Shanghai Telecom, and was compared against standard DRL models, reward-shaping baselines, and heuristic methods. • Proposing a DRL framework that integrates reward shaping for resource management. • Introducing a novel MDP design that considers the dynamic nature of the users. • Presenting a novel reward shaping mechanism, incorporating GCN and transformers.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".