Carla GymDrive is a powerful framework designed to facilitate reinforcement learning experiments in autonomous driving using the Carla simulator. By providing a gymnasium-like environment, it offers an intuitive and efficient platform for training driving agents using reinforcement learning techniques.
Carla GymDrive seamlessly integrates with the Carla simulator, allowing users to leverage its extensive features for creating realistic driving scenarios. It also provides an easy to customize environment for fine-tuning the simulation parameters to the user's needs.
With built-in compatibility for reinforcement learning libraries such as Stable Baselines3, Carla GymDrive streamlines the process of training autonomous driving agents.
The framework is built with a modular design approach, enabling easy customization and integration into various projects and workflows.
By leveraging json files, it is possible to create various builds of vehicles with different sensors and configurations. This allows for the creation of custom vehicles with different sensor configurations. Such example of a build can be found in the ./env/train_sensors.json file.
Through Pygame, it is possible to visualize the sensor data in real-time. This is useful for debugging and testing purposes.
Vehicles have their physics changed according to the weather. This template allows for the customization of a vehicle's physics based on the weather conditions. This is useful for simulating the effects of weather on a vehicle's performance. This can be achieved through JSON files. One such example can be found in the ./env/default_vehicle_physics.json file.
The template allows for the complete control and management of the Carla simulator using minimal code. This is achieved through the use of the World class. This class allows for the easy management of the Carla simulator, such as changing the map, the weather, and even spawning traffic and pedestrians.
- It is recommended to use a virtual environment with python 3.8.
- If you wish to use the same virtual environment as me which used Carla 0.9.15, install conda and run
conda env create -f environment.yml - If you wish to use a different version of Carla, you can create a new environment with
conda create -n carla python=3.8and then install the requirements withpip install -r requirements.txt
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Setup the environment variable
CARLA_SERVERto the path of the Carla server directory. -
Run the Carla server:
- If the script automatically starts the server, you can skip this step. Make sure to set the environment variable
CARLA_SERVERto the path of the Carla server directory. - If the script does not automatically start the server, you need to start the server manually.
- Run training/testing scripts
For a more extensive documentation on how to generate episodes, please refer to the Environment documentation.
This template's modules are located and documented in the src directory. Their documentation can be found here
- The simulator may crash when changing maps to many times. This is a known issue with Carla and is not a problem with the template. The problem is random, so if it happens, it is recommended to save checkpoints and then restart the training in the latest checkpoint.
- If the simulator is ran in low quality mode, it crashes the program, this is a problem in Carla's side and it's known by the community;
- Moving the walkers causes segmentation fault. This is a known problem between the community.
- Simply spawning the walkers might cause the program to crash. This is maybe due to my personal computer's performance. I haven't tested it in a more powerfull pc. I don't think it is a coding problem, but i might be wrong.
Carla GymDrive is licensed under the MIT License. See the LICENSE file for details.
Carla GymDrive is inspired by the open-source community and contributions from researchers and developers around the world. We would like to express our gratitude to the Carla team for providing an excellent simulator for autonomous driving research.