<img src="docs/_static/logo.png" align="right" width="30%"/>
[](https://zenodo.org/doi/10.5281/zenodo.10869789)
[](https://github.com/LucasAlegre/sumo-rl/actions/workflows/linux-test.yml)
[](https://badge.fury.io/py/sumo-rl)
[](https://pre-commit.com/)
[](https://github.com/psf/black)
[](https://github.com/LucasAlegre/sumo-rl/blob/main/LICENSE)
# SUMO-RL
<!-- start intro -->
SUMO-RL provides a simple interface to instantiate Reinforcement Learning (RL) environments with [SUMO](https://github.com/eclipse/sumo) for Traffic Signal Control.
Goals of this repository:
- Provide a simple interface to work with Reinforcement Learning for Traffic Signal Control using SUMO
- Support Multiagent RL
- Compatibility with gymnasium.Env and popular RL libraries such as [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) and [RLlib](https://docs.ray.io/en/main/rllib.html)
- Easy customisation: state and reward definitions are easily modifiable
The main class is [SumoEnvironment](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/env.py).
If instantiated with parameter 'single-agent=True', it behaves like a regular [Gymnasium Env](https://github.com/Farama-Foundation/Gymnasium).
For multiagent environments, use [env](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/env.py) or [parallel_env](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/env.py) to instantiate a [PettingZoo](https://github.com/PettingZoo-Team/PettingZoo) environment with AEC or Parallel API, respectively.
[TrafficSignal](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/traffic_signal.py) is responsible for retrieving information and actuating on traffic lights using [TraCI](https://sumo.dlr.de/wiki/TraCI) API.
For more details, check the [documentation online](https://lucasalegre.github.io/sumo-rl/).
<!-- end intro -->
## Install
<!-- start install -->
### Install SUMO latest version:
```bash
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)
```bash
echo 'export SUMO_HOME="/usr/share/sumo"' >> ~/.bashrc
source ~/.bashrc
```
Important: for a huge performance boost (~8x) with Libsumo, you can declare the variable:
```bash
export LIBSUMO_AS_TRACI=1
```
Notice that you will not be able to run with sumo-gui or with multiple simulations in parallel if this is active ([more details](https://sumo.dlr.de/docs/Libsumo.html)).
### Install SUMO-RL
Stable release version is available through pip
```bash
pip install sumo-rl
```
Alternatively, you can install using the latest (unreleased) version
```bash
git clone https://github.com/LucasAlegre/sumo-rl
cd sumo-rl
pip install -e .
```
<!-- end install -->
## MDP - Observations, Actions and Rewards
### Observation
<!-- start observation -->
The default observation for each traffic signal agent is a vector:
```python
obs = [phase_one_hot, min_green, lane_1_density,...,lane_n_density, lane_1_queue,...,lane_n_queue]
```
- ```phase_one_hot``` is a one-hot encoded vector indicating the current active green phase
- ```min_green``` is a binary variable indicating whether min_green seconds have already passed in the current phase
- ```lane_i_density``` is the number of vehicles in incoming lane i dividided by the total capacity of the lane
- ```lane_i_queue```is the number of queued (speed below 0.1 m/s) vehicles in incoming lane i divided by the total capacity of the lane
You can define your own observation by implementing a class that inherits from [ObservationFunction](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/observations.py) and passing it to the environment constructor.
<!-- end observation -->
### Action
<!-- start action -->
The action space is discrete.
Every 'delta_time' seconds, each traffic signal agent can choose the next green phase configuration.
E.g.: In the [2-way single intersection](https://github.com/LucasAlegre/sumo-rl/blob/main/experiments/dqn_2way-single-intersection.py) there are |A| = 4 discrete actions, corresponding to the following green phase configurations:
<p align="center">
<img src="docs/_static/actions.png" align="center" width="75%"/>
</p>
Important: every time a phase change occurs, the next phase is preeceded by a yellow phase lasting ```yellow_time``` seconds.
<!-- end action -->
### Rewards
<!-- start reward -->
The default reward function is the change in cumulative vehicle delay:
<p align="center">
<img src="docs/_static/reward.png" align="center" width="25%"/>
</p>
That is, the reward is how much the total delay (sum of the waiting times of all approaching vehicles) changed in relation to the previous time-step.
You can choose a different reward function (see the ones implemented in [TrafficSignal](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/traffic_signal.py)) with the parameter `reward_fn` in the [SumoEnvironment](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/env.py) constructor.
It is also possible to implement your own reward function:
```python
def my_reward_fn(traffic_signal):
return traffic_signal.get_average_speed()
env = SumoEnvironment(..., reward_fn=my_reward_fn)
```
<!-- end reward -->
## API's (Gymnasium and PettingZoo)
### Gymnasium Single-Agent API
<!-- start gymnasium -->
If your network only has ONE traffic light, then you can instantiate a standard Gymnasium env (see [Gymnasium API](https://gymnasium.farama.org/api/env/)):
```python
import gymnasium as gym
import sumo_rl
env = gym.make('sumo-rl-v0',
net_file='path_to_your_network.net.xml',
route_file='path_to_your_routefile.rou.xml',
out_csv_name='path_to_output.csv',
use_gui=True,
num_seconds=100000)
obs, info = env.reset()
done = False
while not done:
next_obs, reward, terminated, truncated, info = env.step(env.action_space.sample())
done = terminated or truncated
```
<!-- end gymnasium -->
### PettingZoo Multi-Agent API
<!-- start pettingzoo -->
For multi-agent environments, you can use the PettingZoo API (see [Petting Zoo API](https://pettingzoo.farama.org/api/parallel/)):
```python
import sumo_rl
env = sumo_rl.parallel_env(net_file='nets/RESCO/grid4x4/grid4x4.net.xml',
route_file='nets/RESCO/grid4x4/grid4x4_1.rou.xml',
use_gui=True,
num_seconds=3600)
observations = env.reset()
while env.agents:
actions = {agent: env.action_space(agent).sample() for agent in env.agents} # this is where you would insert your policy
observations, rewards, terminations, truncations, infos = env.step(actions)
```
<!-- end pettingzoo -->
### RESCO Benchmarks
In the folder [nets/RESCO](https://github.com/LucasAlegre/sumo-rl/tree/main/nets/RESCO) you can find the network and route files from [RESCO](https://github.com/jault/RESCO) (Reinforcement Learning Benchmarks for Traffic Signal Control), which was built on top of SUMO-RL. See their [paper](https://people.engr.tamu.edu/guni/Papers/NeurIPS-signals.pdf) for results.
<p align="center">
<img src="nets/RESCO/maps.png" align="center" width="60%"/>
</p>
### Experiments
Check [experiments](https://github.com/LucasAlegre/sumo-rl/tree/main/experiments) for examples on how to instantiate an environment and train your RL agent.
### [Q-learning](https://github.com/LucasAlegre/sumo-rl/blob/main/agents/ql_agent.py) in a one-way single intersection:
```bash
python experiments/ql_single-intersection.py
```
### [RLlib PPO](https://docs.ray.io/en/latest/_modules/ray/rllib/algorithms/ppo/ppo.html) multiagent in a 4x4 grid:
```bash
python experiments/ppo_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:
Obs: you need to install stable-baselines3 with ```pip install "stable_baselines3[extra]>=2.0.0a9"``` for [Gymnasium compatibility](https://stable-baselines3.readthedocs.io/en/master/guide/install.html).
```bash
python experiments/dqn_2way-single-intersection.py
```
### Plotting results:
```bash
python outputs/plot.py -f outputs/4x4grid/ppo_conn0_ep2
```
<p align="center">
<img src="outputs/result.png" align="center" width="50%"/>
</p>
## Citing
<!-- start citation -->
If you use this repository in your research, please cite:
```bibtex
@misc{sumorl,
author = {Lucas N. Alegre},
title = {{SUMO-RL}},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LucasAlegre/sumo-rl}},
}
```
<!-- end citation -->
<!-- start list of publications -->
List of publications that use SUMO-RL (please open a pull request to add missing entries):
- [Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control (Alegre et al., 2021)](https://peerj.com/articles/cs-575/)
- [Information-Theoretic State Space Model for Multi-View Reinforcement Learning (Hwang et al., 2023)](https://openreview.net/forum?id=jwy77xkyPt)
- [A citywide TD-learning based intelligent traffic signal control for autonomous vehicles: Performance evaluation using SUMO (Reza et al., 2023)](https://onlinelibrary.wiley.com/doi/full/10.1111/exsy.13301)
- [Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model (Ghanadbashi et al., 2023)](https://link.springer.com/article/10.1007/s40860-022-00198-x)
- [Multiagent Reinforcement Learning for Traffic Signal Control: a k-Nearest Neighbors Based Approach (Almeida et al., 2022)](https://ceur-ws.org/Vol-3173/3.pdf)
- [From Local to Global: A Curriculum Learning Approach for Reinforcement Learning-based Traffic Signal Control (Zheng et al., 2022)](https://ieeexplore.ieee.org/abstract/document/9832372)
- [Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning (Bagwe et al., 2022)](https://ieeexplore.ieee.org/abstract/document/9996639)
- [Using ontology to guide reinforcement learning agents in unseen situations (Ghanadbashi & Golpayegani, 2022)](https://link.springer.com/article/10.1007/s10489-021-02449-5)
- [Information upwards, recommendation downwards: reinforcement learning with hierarchy for traffic signal control (Antes et al., 2022)](https://www.sciencedirect.com/science/article/pii/S1877050922004185)
- [A Comparative Study of Algorithms for Intelligent Traffic Signal Control (Chaudhuri et al., 2022)](https://link.springer.com/chapter/10.1007/978-981-16-7996-4_19)
- [An Ontology-Based Intelligent Traffic Signal Control Model (Ghanadbashi & Golpayegani, 2021)](https://ieeexplore.ieee.org/abstract/document/9564962)
- [Reinforcement Learning Benchmarks for Traffic Signal Control (Ault & Sharon, 2021)](https://openreview.net/forum?id=LqRSh6V0vR)
- [EcoLight: Reward Shaping in Deep Reinforcement Learning for Ergonomic Traffic Signal Control (Agand et al., 2021)](https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/neurips2021/43/paper.pdf)
<!-- end list of publications -->
Raw data
{
"_id": null,
"home_page": null,
"name": "sumo-rl",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "Reinforcement Learning, Traffic Signal Control, SUMO, RL, PettingZoo, gymnasium",
"author": null,
"author_email": "Lucas Alegre <lucasnale@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/e9/76/892e7b2e314887f21958a9c5b215c689a2e50cc134e49ec6654efef66751/sumo_rl-1.4.5.tar.gz",
"platform": null,
"description": "<img src=\"docs/_static/logo.png\" align=\"right\" width=\"30%\"/>\n\n[](https://zenodo.org/doi/10.5281/zenodo.10869789)\n[](https://github.com/LucasAlegre/sumo-rl/actions/workflows/linux-test.yml)\n[](https://badge.fury.io/py/sumo-rl)\n[](https://pre-commit.com/)\n[](https://github.com/psf/black)\n[](https://github.com/LucasAlegre/sumo-rl/blob/main/LICENSE)\n\n# SUMO-RL\n\n<!-- start intro -->\n\nSUMO-RL provides a simple interface to instantiate Reinforcement Learning (RL) environments with [SUMO](https://github.com/eclipse/sumo) for Traffic Signal Control.\n\nGoals of this repository:\n- Provide a simple interface to work with Reinforcement Learning for Traffic Signal Control using SUMO\n- Support Multiagent RL\n- Compatibility with gymnasium.Env and popular RL libraries such as [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) and [RLlib](https://docs.ray.io/en/main/rllib.html)\n- Easy customisation: state and reward definitions are easily modifiable\n\nThe main class is [SumoEnvironment](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/env.py).\nIf instantiated with parameter 'single-agent=True', it behaves like a regular [Gymnasium Env](https://github.com/Farama-Foundation/Gymnasium).\nFor multiagent environments, use [env](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/env.py) or [parallel_env](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/env.py) to instantiate a [PettingZoo](https://github.com/PettingZoo-Team/PettingZoo) environment with AEC or Parallel API, respectively.\n[TrafficSignal](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/traffic_signal.py) is responsible for retrieving information and actuating on traffic lights using [TraCI](https://sumo.dlr.de/wiki/TraCI) API.\n\nFor more details, check the [documentation online](https://lucasalegre.github.io/sumo-rl/).\n\n<!-- end intro -->\n\n## Install\n\n<!-- start install -->\n\n### Install SUMO latest version:\n\n```bash\nsudo add-apt-repository ppa:sumo/stable\nsudo apt-get update\nsudo apt-get install sumo sumo-tools sumo-doc\n```\nDon't forget to set SUMO_HOME variable (default sumo installation path is /usr/share/sumo)\n```bash\necho 'export SUMO_HOME=\"/usr/share/sumo\"' >> ~/.bashrc\nsource ~/.bashrc\n```\nImportant: for a huge performance boost (~8x) with Libsumo, you can declare the variable:\n```bash\nexport LIBSUMO_AS_TRACI=1\n```\nNotice that you will not be able to run with sumo-gui or with multiple simulations in parallel if this is active ([more details](https://sumo.dlr.de/docs/Libsumo.html)).\n\n### Install SUMO-RL\n\nStable release version is available through pip\n```bash\npip install sumo-rl\n```\n\nAlternatively, you can install using the latest (unreleased) version\n```bash\ngit clone https://github.com/LucasAlegre/sumo-rl\ncd sumo-rl\npip install -e .\n```\n\n<!-- end install -->\n\n## MDP - Observations, Actions and Rewards\n\n### Observation\n\n<!-- start observation -->\n\nThe default observation for each traffic signal agent is a vector:\n```python\n obs = [phase_one_hot, min_green, lane_1_density,...,lane_n_density, lane_1_queue,...,lane_n_queue]\n```\n- ```phase_one_hot``` is a one-hot encoded vector indicating the current active green phase\n- ```min_green``` is a binary variable indicating whether min_green seconds have already passed in the current phase\n- ```lane_i_density``` is the number of vehicles in incoming lane i dividided by the total capacity of the lane\n- ```lane_i_queue```is the number of queued (speed below 0.1 m/s) vehicles in incoming lane i divided by the total capacity of the lane\n\nYou can define your own observation by implementing a class that inherits from [ObservationFunction](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/observations.py) and passing it to the environment constructor.\n\n<!-- end observation -->\n\n### Action\n\n<!-- start action -->\n\nThe action space is discrete.\nEvery 'delta_time' seconds, each traffic signal agent can choose the next green phase configuration.\n\nE.g.: In the [2-way single intersection](https://github.com/LucasAlegre/sumo-rl/blob/main/experiments/dqn_2way-single-intersection.py) there are |A| = 4 discrete actions, corresponding to the following green phase configurations:\n\n<p align=\"center\">\n<img src=\"docs/_static/actions.png\" align=\"center\" width=\"75%\"/>\n</p>\n\nImportant: every time a phase change occurs, the next phase is preeceded by a yellow phase lasting ```yellow_time``` seconds.\n\n<!-- end action -->\n\n### Rewards\n\n<!-- start reward -->\n\nThe default reward function is the change in cumulative vehicle delay:\n\n<p align=\"center\">\n<img src=\"docs/_static/reward.png\" align=\"center\" width=\"25%\"/>\n</p>\n\nThat is, the reward is how much the total delay (sum of the waiting times of all approaching vehicles) changed in relation to the previous time-step.\n\nYou can choose a different reward function (see the ones implemented in [TrafficSignal](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/traffic_signal.py)) with the parameter `reward_fn` in the [SumoEnvironment](https://github.com/LucasAlegre/sumo-rl/blob/main/sumo_rl/environment/env.py) constructor.\n\nIt is also possible to implement your own reward function:\n\n```python\ndef my_reward_fn(traffic_signal):\n return traffic_signal.get_average_speed()\n\nenv = SumoEnvironment(..., reward_fn=my_reward_fn)\n```\n\n<!-- end reward -->\n\n## API's (Gymnasium and PettingZoo)\n\n### Gymnasium Single-Agent API\n\n<!-- start gymnasium -->\n\nIf your network only has ONE traffic light, then you can instantiate a standard Gymnasium env (see [Gymnasium API](https://gymnasium.farama.org/api/env/)):\n```python\nimport gymnasium as gym\nimport sumo_rl\nenv = gym.make('sumo-rl-v0',\n net_file='path_to_your_network.net.xml',\n route_file='path_to_your_routefile.rou.xml',\n out_csv_name='path_to_output.csv',\n use_gui=True,\n num_seconds=100000)\nobs, info = env.reset()\ndone = False\nwhile not done:\n next_obs, reward, terminated, truncated, info = env.step(env.action_space.sample())\n done = terminated or truncated\n```\n\n<!-- end gymnasium -->\n\n### PettingZoo Multi-Agent API\n\n<!-- start pettingzoo -->\n\nFor multi-agent environments, you can use the PettingZoo API (see [Petting Zoo API](https://pettingzoo.farama.org/api/parallel/)):\n\n```python\nimport sumo_rl\nenv = sumo_rl.parallel_env(net_file='nets/RESCO/grid4x4/grid4x4.net.xml',\n route_file='nets/RESCO/grid4x4/grid4x4_1.rou.xml',\n use_gui=True,\n num_seconds=3600)\nobservations = env.reset()\nwhile env.agents:\n actions = {agent: env.action_space(agent).sample() for agent in env.agents} # this is where you would insert your policy\n observations, rewards, terminations, truncations, infos = env.step(actions)\n```\n\n<!-- end pettingzoo -->\n\n### RESCO Benchmarks\n\nIn the folder [nets/RESCO](https://github.com/LucasAlegre/sumo-rl/tree/main/nets/RESCO) you can find the network and route files from [RESCO](https://github.com/jault/RESCO) (Reinforcement Learning Benchmarks for Traffic Signal Control), which was built on top of SUMO-RL. See their [paper](https://people.engr.tamu.edu/guni/Papers/NeurIPS-signals.pdf) for results.\n\n<p align=\"center\">\n<img src=\"nets/RESCO/maps.png\" align=\"center\" width=\"60%\"/>\n</p>\n\n### Experiments\n\nCheck [experiments](https://github.com/LucasAlegre/sumo-rl/tree/main/experiments) for examples on how to instantiate an environment and train your RL agent.\n\n### [Q-learning](https://github.com/LucasAlegre/sumo-rl/blob/main/agents/ql_agent.py) in a one-way single intersection:\n```bash\npython experiments/ql_single-intersection.py\n```\n\n### [RLlib PPO](https://docs.ray.io/en/latest/_modules/ray/rllib/algorithms/ppo/ppo.html) multiagent in a 4x4 grid:\n```bash\npython experiments/ppo_4x4grid.py\n```\n\n### [stable-baselines3 DQN](https://github.com/DLR-RM/stable-baselines3/blob/master/stable_baselines3/dqn/dqn.py) in a 2-way single intersection:\nObs: you need to install stable-baselines3 with ```pip install \"stable_baselines3[extra]>=2.0.0a9\"``` for [Gymnasium compatibility](https://stable-baselines3.readthedocs.io/en/master/guide/install.html).\n```bash\npython experiments/dqn_2way-single-intersection.py\n```\n\n### Plotting results:\n```bash\npython outputs/plot.py -f outputs/4x4grid/ppo_conn0_ep2\n```\n<p align=\"center\">\n<img src=\"outputs/result.png\" align=\"center\" width=\"50%\"/>\n</p>\n\n## Citing\n\n<!-- start citation -->\n\nIf you use this repository in your research, please cite:\n```bibtex\n@misc{sumorl,\n author = {Lucas N. Alegre},\n title = {{SUMO-RL}},\n year = {2019},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/LucasAlegre/sumo-rl}},\n}\n```\n\n<!-- end citation -->\n\n<!-- start list of publications -->\n\nList of publications that use SUMO-RL (please open a pull request to add missing entries):\n- [Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control (Alegre et al., 2021)](https://peerj.com/articles/cs-575/)\n- [Information-Theoretic State Space Model for Multi-View Reinforcement Learning (Hwang et al., 2023)](https://openreview.net/forum?id=jwy77xkyPt)\n- [A citywide TD-learning based intelligent traffic signal control for autonomous vehicles: Performance evaluation using SUMO (Reza et al., 2023)](https://onlinelibrary.wiley.com/doi/full/10.1111/exsy.13301)\n- [Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model (Ghanadbashi et al., 2023)](https://link.springer.com/article/10.1007/s40860-022-00198-x)\n- [Multiagent Reinforcement Learning for Traffic Signal Control: a k-Nearest Neighbors Based Approach (Almeida et al., 2022)](https://ceur-ws.org/Vol-3173/3.pdf)\n- [From Local to Global: A Curriculum Learning Approach for Reinforcement Learning-based Traffic Signal Control (Zheng et al., 2022)](https://ieeexplore.ieee.org/abstract/document/9832372)\n- [Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning (Bagwe et al., 2022)](https://ieeexplore.ieee.org/abstract/document/9996639)\n- [Using ontology to guide reinforcement learning agents in unseen situations (Ghanadbashi & Golpayegani, 2022)](https://link.springer.com/article/10.1007/s10489-021-02449-5)\n- [Information upwards, recommendation downwards: reinforcement learning with hierarchy for traffic signal control (Antes et al., 2022)](https://www.sciencedirect.com/science/article/pii/S1877050922004185)\n- [A Comparative Study of Algorithms for Intelligent Traffic Signal Control (Chaudhuri et al., 2022)](https://link.springer.com/chapter/10.1007/978-981-16-7996-4_19)\n- [An Ontology-Based Intelligent Traffic Signal Control Model (Ghanadbashi & Golpayegani, 2021)](https://ieeexplore.ieee.org/abstract/document/9564962)\n- [Reinforcement Learning Benchmarks for Traffic Signal Control (Ault & Sharon, 2021)](https://openreview.net/forum?id=LqRSh6V0vR)\n- [EcoLight: Reward Shaping in Deep Reinforcement Learning for Ergonomic Traffic Signal Control (Agand et al., 2021)](https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/neurips2021/43/paper.pdf)\n\n<!-- end list of publications -->\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "RL environments and learning code for traffic signal control in SUMO.",
"version": "1.4.5",
"project_urls": {
"Bug Report": "https://github.com/LucasAlegre/sumo-rl/issues",
"Documentation": "https://lucasalegre.github.io/sumo-rl",
"Homepage": "https://lucasalegre.github.io/sumo-rl",
"Repository": "https://github.com/LucasAlegre/sumo-rl"
},
"split_keywords": [
"reinforcement learning",
" traffic signal control",
" sumo",
" rl",
" pettingzoo",
" gymnasium"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "3ed30cf60a0398f14d508b5bc8b80ab0dac73c6fd3a14a8751b37f6c19d27c30",
"md5": "e184db326873e8f788838c3bbd93ed24",
"sha256": "322d090e6a66d97cc9b265c0b6fa092687bc6b35965fe5f53f678878d1987660"
},
"downloads": -1,
"filename": "sumo_rl-1.4.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e184db326873e8f788838c3bbd93ed24",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 1145381,
"upload_time": "2024-05-07T14:51:30",
"upload_time_iso_8601": "2024-05-07T14:51:30.372900Z",
"url": "https://files.pythonhosted.org/packages/3e/d3/0cf60a0398f14d508b5bc8b80ab0dac73c6fd3a14a8751b37f6c19d27c30/sumo_rl-1.4.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "e976892e7b2e314887f21958a9c5b215c689a2e50cc134e49ec6654efef66751",
"md5": "fd912e9246d6fd03c86f6a3c37683c70",
"sha256": "230979fb77969a76839cd36027db2ac1aea0e2a41d9b38000f592287c3d12c11"
},
"downloads": -1,
"filename": "sumo_rl-1.4.5.tar.gz",
"has_sig": false,
"md5_digest": "fd912e9246d6fd03c86f6a3c37683c70",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 1092820,
"upload_time": "2024-05-07T14:51:35",
"upload_time_iso_8601": "2024-05-07T14:51:35.294229Z",
"url": "https://files.pythonhosted.org/packages/e9/76/892e7b2e314887f21958a9c5b215c689a2e50cc134e49ec6654efef66751/sumo_rl-1.4.5.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-05-07 14:51:35",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "LucasAlegre",
"github_project": "sumo-rl",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "sumo-rl"
}
JSON
