# sumolights

**Repository Path**: trafficcoder/sumolights

## Basic Information

- **Project Name**: sumolights
- **Description**: SUMO adaptive traffic signal control - DQN, DDPG, Webster's, Max-pressure, Self-Organizing Traffic Lights
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2020-12-13
- **Last Updated**: 2021-12-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# sumolights
SUMO adaptive traffic signal control - DQN, DDPG, Webster's, Max-pressure, Self-Organizing Traffic Lights

Technical details available at [An Open-Source Framework for Adaptive Traffic Signal Control](https://arxiv.org/abs/1909.00395)

## Setup

Install SUMO traffic microsimulator by [following instructions here](https://sumo.dlr.de/wiki/Installing) (v1.2).

Using Python 3, create a virtual environment and then install dependancies with:
```
pip install -r requirements.txt
```

## Comparing adaptive traffic signal controllers
First train reinforcement learning controllers:
```
./train_dqn.sh
./train_ddpg.sh
```

Then execute simulations to generate performance results for all controllers:
```
./gen_results.sh
```

Visualize results with:
```
python graph_results.py
```
![Screenshot](samples/travel_time.png)
![Screenshot](samples/intersection_moe.png)

## Optimizing hyperparameters
Search for optimal hyperparameters for each controller:
```
./hp_optimization
```
Warning, search for reinforcement learning can require significant compute time!

Visualize hyperparameters with:
```
python graph_results.py -type hp
```
![Screenshot](samples/tsc_hp.png)
![Screenshot](samples/hp.png)