seudarwin/sumo-rl

A simple interface to instantiate Reinforcement Learning environments with SUMO for Traffic Signal Control. Compatible with Gym Env from OpenAI and MultiAgentEnv from RLlib.

SUMO-RL

SUMO-RL provides a simple interface to instantiate Reinforcement Learning environments with SUMO v.1.2.0 for Traffic Signal Control.

The main class SumoEnvironment inherits MultiAgentEnv from RLlib.
If instantiated with parameter 'single-agent=True', it behaves like a regular Gym Env from OpenAI.
TrafficSignal is responsible for retrieving information and actuating on traffic lights using 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 like openAI baselines and RLlib.
• Easy customisation: state and reward definitions are easily modifiable.

Install

To install SUMO v1.2.0:

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

To install sumo_rl package:

pip3 install -e .

Examples

Check experiments to see how to instantiate a SumoEnvironment and use it with your RL algorithm.

Q-learning in a one-way single intersection:

python3 experiments/ql_single-intersection.py 

RLlib A3C multiagent in a 4x4 grid:

python3 experiments/a3c_4x4grid.py

stable-baselines A2C in a 2-way single intersection:

python3 experiments/a2c_2way-single-intersection.py

To plot results:

python3 outputs/plot.py -f outputs/single-intersection_rewardqueue_run1.csv

Cite

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}},
}