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multi-agent deep reinforcement learning for large-scale traffic signal control.
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| 2026-07-31 | 425 |
| 2026-08-06 | 425 |
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# Deep RL for traffic signal control
[](https://opensource.org/licenses/MIT)
This repo implements start-of-the-art mutli-agent (decentralized) deep RL algorithms for large-scale traffic signal control in SUMO-simulated environments.
Available cooperation levels:
* Centralized: a global agent that makes global control w/ global observation, reward.
* Decentralized: multiple local agents that make local control independently w/ neighborhood information sharing.
Available NN layers:
Fully-connected, LSTM.
Available algorithms:
IQL, IA2C, IA2C with stabilization (called MA2C in this paper). For more advanced algorithms, please check [deeprl_network](https://github.com/cts198859/deeprl_network).
Available environments:
* A 6-intersection benchmark traffic network. [Ye, Bao-Lin, et al. "A hierarchical model predictive control approach for signal splits optimization in large-scale urban road networks." IEEE Transactions on Intelligent Transportation Systems 17.8 (2016): 2182-2192.](https://ieeexplore.ieee.org/abstract/document/7406703/)
* A 5X5 traffic grid. [Chu, Tianshu, Shuhui Qu, and Jie Wang. "Large-scale traffic grid signal control with regional reinforcement learning." American Control Conference (ACC), 2016. IEEE, 2016.](https://ieeexplore.ieee.org/abstract/document/7525014/)
* A modified Monaco traffic network with 30 signalized intersections. [L. Codeca, J. Härri, "Monaco SUMO Traffic (MoST) Scenario: A 3D Mobility Scenario for Cooperative ITS" SUMO 2018, SUMO User Conference, Simulating Autonomous and Intermodal Transport Systems May 14-16, 2018, Berlin, Germany.](http://www.eurecom.fr/en/publication/5527/download/comsys-publi-5527.pdf) ([code](https://github.com/lcodeca/MoSTScenario))
## Requirements
* Python3==3.5
* [Tensorflow](http://www.tensorflow.org/install)==1.12.0
* [SUMO](http://sumo.dlr.de/wiki/Installing)>=1.1.0
Required packages can be installed by running `setup_mac.sh` or `setup_ubuntu.sh`.
Attention: the code on master branch is for SUMO version >= 1.1.0. Please go to branch [sumo-0.32.0](https://github.com/cts198859/deeprl_signal_control/tree/sumo-0.32.0) if you are using the old SUMO version.
## Usages
First define all hyperparameters in a config file under `[config_dir]`, and create the base directory of experiements `[base_dir]`. Before training, please call `build_file.py` under `[environment_dir]/data/` to generate SUMO network files for `small_grid` and `large_grid` environments.
1. To train a new agent, run
~~~
python3 main.py --base-dir [base_dir]/[agent] train --config-dir [config_dir] --test-mode no_test
~~~
`[agent]` is from `{ia2c, ma2c, iqll, iqld}`. `no_test` is suggested, since tests will significantly slow down the training speed.
2. To access tensorboard during training, run
~~~
tensorboard --logdir=[base_dir]/log
~~~
3. To evaluate and compare trained agents, run
~~~
python3 main.py --base-dir [base_dir] evaluate --agents [agents] --evaluation-seeds [seeds]
~~~
Evaluation data will be output to `[base_dir]/eva_data`, and make sure evaluation seeds are different from those used in training. Under default evaluation setting, the inference policy of A2C is stochastic whereas that of Q-learning is greedy (deterministic). To explicitly specifiy the inference policy type, pass argument `--evaluation-policy-type [default/stochastic/deterministic]`. Please note running a determinisitc inference policy for A2C may cause the performance loss, due to the violation of "on-policy" learning.
4. To visualize the agent behavior, run
~~~
python3 main.py --base-dir [base_dir] evaluate --agents [agent] --evaluation-seeds [seed] --demo
~~~
It is recommended to have only one agent and one evaluation seed for the demo run. This will launch the SUMO GUI, and `./large_grid/data/view.xml` can be applied to visualize queue length and intersectin delay in edge color and thickness. Below are a few example screenshots.
| t=1500Excerpt of 5,677 characters
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