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Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:
| Date | Stars |
|---|---|
| 2026-07-31 | 373 |
| 2026-08-06 | 373 |
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[MACAD-Gym](https://arxiv.org/abs/1911.04175) is a training platform for Multi-Agent Connected Autonomous
Driving (MACAD) built on top of the CARLA Autonomous Driving simulator.
MACAD-Gym provides OpenAI Gym-compatible learning environments for various
driving scenarios for training Deep RL algorithms in homogeneous/heterogenous,
communicating/non-communicating and other multi-agent settings. New environments and scenarios
can be easily added using a simple, JSON-like configuration.
[](https://pypi.python.org/pypi/macad-gym/)
[](https://pypi.python.org/pypi/macad-gym/)
[](https://pepy.tech/project/macad-gym)
### Quick Start
Install MACAD-Gym using `pip install macad-gym`.
If you have `CARLA_SERVER` setup, you can get going using the following 3 lines of code. If not, follow the
[Getting started steps](#getting-started).
#### Training RL Agents
```python
import gym
import macad_gym
env = gym.make("HomoNcomIndePOIntrxMASS3CTWN3-v0")
# Your agent code here
```
Any RL library that supports the OpenAI-Gym API can be used to train agents in MACAD-Gym. The [MACAD-Agents](https://github.com/praveen-palanisamy/macad-agents) repository provides sample agents as a starter.
#### Visualizing the Environment
To test-drive the environments, you can run the environment script directly. For example, to test-drive the `HomoNcomIndePOIntrxMASS3CTWN3-v0` environment, run:
```bash
python -m macad_gym.envs.homo.ncom.inde.po.intrx.ma.stop_sign_3c_town03
```
### Usage guide
- [Getting Started](#getting-started)
- [Learning Platform and Agent Interface](#learning-platform-and-agent-interface)
- [Environments](#environments)
- [Agent interface](#agent-interface)
- [Citing MACAD-Gym](#citing)
- [Developer Contribution Guide](CONTRIBUTING.md)
### Getting Started
> Assumes an Ubuntu (18.04/20.04/22.04 or later) system.
> If you are on Windows 10/11, use the CARLA Windows package and set the `CARLA_SERVER` environment variable to the CARLA installation directory.
1. Install the system requirements:
- Miniconda/Anaconda 3.x
- `wget -P ~ https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh; bash ~/Miniconda3-latest-Linux-x86_64.sh`
- cmake (`sudo apt install cmake`)
- zlib (`sudo apt install zlib1g-dev`)
- [optional] ffmpeg (`sudo apt install ffmpeg`)
1. Setup CARLA (0.9.x)
3.1 `mkdir ~/software && cd ~/software`
3.2 Example: Download the 0.9.13 release version from: [Here](https://github.com/carla-simulator/carla/releases)
Extract it into `~/software/CARLA_0.9.13`
3.3 `echo "export CARLA_SERVER=${HOME}/software/CARLA_0.9.13/CarlaUE4.sh" >> ~/.bashrc`
1. Install MACAD-Gym:
- **Option1 for users** : `pip install macad-gym`
- **Option2 for developers**:
- Fork/Clone the repository to your workspace:
`git clone https://github.com/praveen-palanisamy/macad-gym.git && cd macad-gym`
- Create a new conda env named "macad-gym" and install the required packages:
`conda env create -f conda_env.yml`
- Activate the `macad-gym` conda python env:
`source activate macad-gym`
- Install the `macad-gym` package:
`pip install -e .`
- Install CARLA PythonAPI: `pip install carla==0.9.13`
> NOTE: Change the carla client PyPI package version number to match with your CARLA server version
### Learning Platform and Agent Interface
The MACAD-Gym platform provides learning environments for training agents in both,
single-agent and multi-agent settings for various autonomous driving tasks and
scenarios that enables training agents in homogeneous/heterogeneous
The learning environments follows naming convention for the ID to be consistent
and to support versioned benchmarking of agent alExcerpt of 11,118 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:236299d583335c67, topic:deep-reinforcement-learning, topic:multi-agent-reinforcement-learning, readme:rl algorithms
matched fp:236299d583335c67, topic:autonomous-driving, desc:autonomous driving, readme:autonomous driving