Multi-Commander
Traffic Signal Control based on Multi & Single Agent Reinforcement learning Algorithms
This is a project for Deecamp 2019 which cooperated with APEX Lib of Shanghai Jiaotong University and Tianrang Intelligence Company
Documentation for Q-Value based Method
DQN | DDQN | DuelingDQN | RayDQN
Documentation for PG based Method
Documentation for Distributed Method
Multi-Agent Method
QMIX | Gamma-Reward | PressLight
Docker image
this is a complete Docker image,you can start work quickly with it.
environment：ubuntu16.04 python3.6 Cityflow tensorflow1.14.0 Ray Gym
you need run your docker container keep port 8080,6006,8265 connected.
docker pull 957001934/road-commander
Installation
you can also built your environment,please follow these steps. This project is based on CityFlow which is a multi-agent reinforcement learning environment for large scale city traffic scenario, the algorithms are bases on Ray which is a fast and simple framework for building and running distributed applications.
CityFlow build from source
This guide is based on Ubuntu 16.04.
CityFlow has little dependencies, so building from source is not scary.
- Check that you have python 3.6 installed. Other version of python might work, however, we only tested on python with version >= 3.6.
- Install cpp dependencies
apt update && apt-get install -y build-essential libboost-all-dev cmake
- Clone CityFlow project from github.
git clone --recursive https://github.com/cityflow-project/CityFlow.git
Notice that CityFlow uses pybind11 to integrate C++ code with python, the repo has pybind11 as a submodule, please use --recursive to clone all codes.
- Go to CityFlow project’s root directory and run
pip install .- Wait for installation to complete and CityFlow should be successfully installed.
import cityflow
eng = cityflow.EngineRay Installation
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U ray # also recommended: ray[debug]Gym
git clone https://github.com/openai/gym
cd gym
pip install -e .