# davidtw0320/Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning

<https://repo.masters-masters.com/en/repos/davidtw0320/Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning>

[Home](https://repo.masters-masters.com/en) › [davidtw0320](https://repo.masters-masters.com/en/owners/davidtw0320) › [Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning](https://repo.masters-masters.com/en/repos/davidtw0320/Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning)

davidtw0320/Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning has 374 stars on GitHub. Its RepoMasters card: Alpha tier.

| Field | Value |
| --- | --- |
| Owner | [davidtw0320](https://repo.masters-masters.com/en/owners/davidtw0320) |
| Stars | 374 |
| Forks | 107 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | None |
| Created | 2020 |
| Topics | resource-allocation, edge-servers, ddpg |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [davidtw0320/Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning](https://github.com/davidtw0320/Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning) |

## Repository

Simulated the scenario between edge servers and users with a clear graphic interface. Also, implemented the continuous control with Deep Deterministic Policy Gradient (DDPG) to determine the resources allocation (offload targets, computational resources, migration bandwidth) in the edge servers

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.
