LucasAlegre / Sumo Rl
Licence: mit
A simple interface to instantiate Reinforcement Learning environments with SUMO for Traffic Signal Control. Compatible with Gym Env from OpenAI and MultiAgentEnv from RLlib.
Stars: ✭ 145
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SUMO-RL
SUMO-RL provides a simple interface to instantiate Reinforcement Learning environments with SUMO 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 such as stable-baselines3 and RLlib
- 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 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-baselines3 DQN 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}},
}
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