sumo-rl:一个简单的界面，用于通过SUMO实例化强化学习环境，以进行交通信号控制。 与OpenAI的Gym Env和RLl...

<img src="outputs/logo.png" align="right" width="30%"/> [](https://www.repostatus.org/#active) [](https://github.com/LucasAlegre/sumo-rl/blob/master/LICENSE) # SUMO-RL SUMO-RL provides a simple interface to instantiate Reinforcement Learning environments with [SUMO](https://github.com/eclipse/sumo) for Traffic Signal Control. The main class [SumoEnvironment](https://github.com/LucasAlegre/sumo-rl/blob/master/environment/env.py) inherits [MultiAgentEnv](https://github.com/ray-project/ray/blob/master/python/ray/rllib/env/multi_agent_env.py) from [RLlib](https://github.com/ray-project/ray/tree/master/python/ray/rllib). If instantiated with parameter 'single-agent=True', it behaves like a regular [Gym Env](https://github.com/openai/gym/blob/master/gym/core.py) from [OpenAI](https://github.com/openai). [TrafficSignal](https://github.com/LucasAlegre/sumo-rl/blob/master/environment/traffic_signal.py) is responsible for retrieving information and actuating on traffic lights using [TraCI](https://sumo.dlr.de/wiki/TraCI) API. Goals of this repository: - Provide a simple interface to work with Reinforcement Learning for Traffic Signal Control using SUMO - Support Multiagent RL - Compatibility with gym.Env and popular RL libraries such as [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) and [RLlib](https://docs.ray.io/en/master/rllib.html) - Easy customisation: state and reward definitions are easily modifiable ## Install ### Install SUMO latest version: ``` sudo add-apt-repository ppa:sumo/stable sudo apt-get update sudo apt-get install sumo sumo-tools sumo-doc ``` Don't forget to set SUMO_HOME variable (default sumo installation path is /usr/share/sumo) ``` echo 'export SUMO_HOME="/usr/share/sumo"' >> ~/.bashrc source ~/.bashrc ``` ### Install SUMO-RL Stable release version is available through pip ``` pip install sumo-rl ``` Alternatively you can install using the latest (unreleased) version ``` git clone https://github.com/LucasAlegre/sumo-rl cd sumo-rl pip install -e . ``` ## Examples Check [experiments](https://github.com/LucasAlegre/sumo-rl/tree/master/experiments) to see how to instantiate a SumoEnvironment and use it with your RL algorithm. ### [Q-learning](https://github.com/LucasAlegre/sumo-rl/blob/master/agents/ql_agent.py) in a one-way single intersection: ``` python3 experiments/ql_single-intersection.py ``` ### [RLlib A3C](https://github.com/ray-project/ray/tree/master/python/ray/rllib/agents/a3c) multiagent in a 4x4 grid: ``` python3 experiments/a3c_4x4grid.py ``` ### [stable-baselines3 DQN](https://github.com/DLR-RM/stable-baselines3/blob/master/stable_baselines3/dqn/dqn.py) in a 2-way single intersection: ``` python3 experiments/dqn_2way-single-intersection.py ``` ### Plotting results: ``` python3 outputs/plot.py -f outputs/2way-single-intersection/a3c ```  ## Citation If you use this repository in your research, please cite: ``` @misc{sumorl, author = {Lucas N. Alegre}, title = {SUMO-RL}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/LucasAlegre/sumo-rl}}, } ```