CoLight：学习交通信号控制的网络级合作_Python_JavaScript_下载.zip

# CoLight CoLight is a reinforcement learning agent for network-level traffic signal control. ``` @inproceedings{colight, author = {Wei, Hua and Xu, Nan and Zhang, Huichu and Zheng, Guanjie and Zang, Xinshi and Chen, Chacha and Zhang, Weinan and Zhu, Yamin and Xu, Kai and Li, Zhenhui}, title = {CoLight: Learning Network-level Cooperation for Traffic Signal Control}, booktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management}, series = {CIKM '19}, year = {2019}, location = {Beijing, China} } ``` It shares the similar code structure with PressLight ([PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network](http://personal.psu.edu/hzw77/publications/presslight-kdd19.pdf)) from KDD 2019. Usage and more information can be found below. ## Usage How to run the code: We recommend to run the code through docker. Some brief documentation can be found at https://docs.docker.com/. 1. Please build a docker image using the dockerfile provided. ``sudo docker pull hzw77/colight:v0.1`` 2. Pull the codes for CoLight. ``git clone https://github.com/wingsweihua/colight.git`` 3. Please run the built docker image to initiate a docker container. Please remember to mount the code directory. ``sudo docker run -it -v /path/to/your/workspace/colight/:/colight/ --shm-size=8gb --name hua_colight hzw77/colight:v0.1 /bin/bash`` ``cd colight`` (Alternatively, you can install the packages (included in the dockerfile) on your linux system) Start an experiment by: ``python -O runexp.py`` Here, ``-O`` option cannot be omitted unless debug is necessary. In the file ``runexp.py``, the args can be changed. * ``runexp.py`` Run the pipeline under different traffic flows. Specific traffic flow files as well as basic configuration can be assigned in this file. For details about config, please turn to ``config.py``. For most cases, you might only modify traffic files and config parameters in ``runexp.py``. ## Dataset * synthetic data Traffic file and road networks can be found in ``data/1_3`` && ``data/3_3`` && ``data/6_6`` && ``data/10_10``. * real-world data Traffic file and road networks of New York City can be found in ``data/NewYork``, it contains two networks at different scale: 196 intersection and 48 intersections. Jinan and Hangzhou dataset are also included. ## Agent * ``agent.py`` An abstract class of different agents. * ``CoLight_agent.py`` Proposed CoLight agent ## Others More details about this project are demonstrated in this part. * ``config.py`` The whole configuration of this project. Note that some parameters will be replaced in ``runexp.py`` while others can only be changed in this file, please be very careful!!! * ``pipeline.py`` The whole pipeline is implemented in this module: Start a simulator environment, run a simulation for certain time(one round), construct samples from raw log data, update the model and model pooling. * ``generator.py`` A generator to load a model, start a simulator enviroment, conduct a simulation and log the results. * ``anon_env.py`` Define a simulator environment to interact with the simulator and obtain needed data like features. * ``construct_sample.py`` * Construct training samples from original data. Select desired state features in the config and compute the corrsponding average/instant reward with specific measure time. * ``updater.py`` Define a class of updater for model updating.