# TJ1812/Adaptive-Traffic-Signal-Control-Using-Reinforcement-Learning

<https://repo.masters-masters.com/en/repos/TJ1812/Adaptive-Traffic-Signal-Control-Using-Reinforcement-Learning>

[Home](https://repo.masters-masters.com/en) › [TJ1812](https://repo.masters-masters.com/en/owners/TJ1812) › [Adaptive-Traffic-Signal-Control-Using-Reinforcement-Learning](https://repo.masters-masters.com/en/repos/TJ1812/Adaptive-Traffic-Signal-Control-Using-Reinforcement-Learning)

TJ1812/Adaptive-Traffic-Signal-Control-Using-Reinforcement-Learning has 157 stars on GitHub. Its RepoMasters card: Prototype tier. Language: Python.

| Field | Value |
| --- | --- |
| Owner | [TJ1812](https://repo.masters-masters.com/en/owners/TJ1812) |
| Stars | 157 |
| Forks | 50 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | None |
| Created | 2018 |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [TJ1812/Adaptive-Traffic-Signal-Control-Using-Reinforcement-Learning](https://github.com/TJ1812/Adaptive-Traffic-Signal-Control-Using-Reinforcement-Learning) |

## Repository

This is an application exploiting principles of Deep Reinforcement Learning. The Deep Neural Network is trained to approximate the Bellman Equation (Q-Learning).

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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