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https://github.com/opendilab/di-smartcross
Decision Intelligence platform for Traffic Crossing Signal Control
https://github.com/opendilab/di-smartcross
reinforcement-learning traffic-light-control traffic-signal-control
Last synced: 5 days ago
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Decision Intelligence platform for Traffic Crossing Signal Control
- Host: GitHub
- URL: https://github.com/opendilab/di-smartcross
- Owner: opendilab
- License: apache-2.0
- Created: 2021-12-20T10:52:15.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-22T03:18:23.000Z (over 1 year ago)
- Last Synced: 2024-01-29T22:58:53.474Z (10 months ago)
- Topics: reinforcement-learning, traffic-light-control, traffic-signal-control
- Language: Python
- Homepage: https://opendilab.github.io/DI-smartcross/
- Size: 9.04 MB
- Stars: 207
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DI-smartcross
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[](https://github.com/opendilab/DI-smartcross/blob/master/LICENSE)
## Introduction
[DI-smartcross doc](https://opendilab.github.io/DI-smartcross/index.html)
**DI-smartcross** is an open-source Decision Intelligence platform for Traffic Crossing Signal Control task. DI-smartcross applies several Reinforcement Learning policies training & evaluation for the traffic signal control system in provided road nets. DI-smartcross is application platform under [OpenDILab](http://opendilab.org/).
DI-smartcross uses [**DI-engine**](https://github.com/opendilab/DI-engine), a Reinforcement Learning platform, to build RL experiments. DI-smartcross uses [SUMO](https://www.eclipse.org/sumo/) (Simulation of Urban MObility) and [CityFlow](https://cityflow-project.github.io) traffic simulator packages to run signal control simulation.
DI-smartcross supports:
- **Single-Agent** and **Multi-Agent** Reinforcement Learning
- **Synthetic** and **Real** roadnet, **Arterial** and **Grid** network shape
- **Customizable** observation, action and reward types
- Easily achieve **Multi-Environment Parallel**, **Actor-Learner Asynchronous Parallel** when training with DI-engine
## Outline
- [Introduction](#introduction)
- [Outline](#outline)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [File Structure](#file-structure)
- [Contributing](#contributing)
- [License](#license)
- [Citation](#citation)
## Installation
DI-smartcross supports SUMO version >= 1.6.0. You can refer to
[SUMO documentation](https://sumo.dlr.de/docs/Installing/index.html) or follow our installation guidance in
[documents](https://opendilab.github.io/DI-smartcross/installation.html).
CityFlow can be installed and compiled from source code. You can clone their repo and run `pip install .`
Then, DI-smartcross is able to be installed from the source code.
Simply run `pip install .` in the root folder of this repository.
This will automatically install [DI-engine](https://github.com/opendilab/DI-engine) as well.
```bash
pip install -e . --user
```
## Quick Start
DI-smartcross provides simple entry for RL training and evaluation. DI-smartcross supports DQN, Off-policy PPO
and Rainbow DQN RL methods with multi-discrete actions for each crossing, as well as multi-agent RL policies
in which each crossing is handled by a individual agent. A set of default DI-engine configs is provided for
each policy. You can check the document of DI-engine to get detailed instructions on these configs.
Here we show RL training sript for sumo envs, same with cityflow env.
- train RL policies
Example of running DQN in sumo wj3 env with default config.
```bash
sumo_train -e smartcross/envs/sumo_wj3_default_config.yaml -d entry/config/sumo_wj3_dqn_default_config.py
```
Example of running PPO in cityflow grid env with default config.
```bash
cityflow_train -e ./smartcross/envs/cityflow_grid/cityflow_grid_config.json -d entry/cityflow_config/cityflow_grid_ppo_default_config.py
```
- evaluate existing policies
Example of running random policy in wj3 env.
```bash
sumo_eval -p random -e smartcross/envs/sumo_wj3_default_config.yaml
```
Example of running fix policy in cityflow grid env.
```bash
cityflow_eval -e smartcross/envs/cityflow_grid/cityflow_auto_grid_config.json -d entry/cityflow_config/cityflow_eval_default_config.py -p fix
```
It is rerecommended to refer to [documation](https://opendilab.github.io/DI-smartcross/index.html)
for detailed information.
## File Structure
```
DI-smartcross
|-- .flake8
|-- .gitignore
|-- .style.yapf
|-- LICENSE
|-- README.md
|-- format.sh
|-- modify_traci_connect_timeout.sh
|-- setup.py
|-- docs
| |-- .gitignore
| |-- Makefile
| |-- figs
| |-- source
|-- entry
| |-- cityflow_eval
| |-- cityflow_train
| |-- sumo_eval
| |-- sumo_train
| |-- cityflow_config
| |-- sumo_config
|-- smartcross
|-- __init__.py
|-- envs
| |-- __init__.py
| |-- cityflow_env.py
| |-- crossing.py
| |-- sumo_arterial7_default_config.yaml
| |-- sumo_arterial7_multi_agent_config.yaml
| |-- sumo_env.py
| |-- sumo_wj3_default_config.yaml
| |-- sumo_wj3_multi_agent_config.yaml
| |-- action
| |-- cityflow_grid
| |-- obs
| |-- reward
| |-- sumo_arterial_7roads
| |-- sumo_wj3
| |-- tests
| |-- test_cityflow_env.py
| |-- test_sumo_env.py
|-- policy
| |-- __init__.py
| |-- default_policy.py
| |-- tests
| |-- test_policy.py
|-- utils
|-- config_utils.py
|-- env_utils.py
```
## Join and Contribute
We appreciate all contributions to improve DI-smartcross, both algorithms and system designs. Welcome to OpenDILab community! Scan the QR code and add us on Wechat:
Or you can contact us with [slack](https://opendilab.slack.com/join/shared_invite/zt-v9tmv4fp-nUBAQEH1_Kuyu_q4plBssQ#/shared-invite/email) or email ([email protected]).
## License
DI-smartcross released under the Apache 2.0 license.
## Citation
```latex
@misc{smartcross,
title={{DI-smartcross: OpenDILab} Decision Intelligence platform for Traffic Crossing Signal Control},
author={DI-smartcross Contributors},
publisher = {GitHub},
howpublished = {\url{https://github.com/opendilab/DI-smartcross}},
year={2021},
}
```