Artificial Intelligence (AI) is making significant advances across various technology sectors, and one of the most exciting areas is self-driving cars. In this article, we will explore how to train an agent to drive autonomously using a Deep Reinforcement Learning (DRL) approach. Our training environment will utilize the open-source simulator CARLA, which creates a hyper-realistic urban simulation to conduct our experiments.

About the Project

This project aims to develop an end-to-end solution for autonomous driving. Our task is divided into three main components:

• CARLA Environment setup.
• Variational Autoencoder implementation.
• Proximal Policy Optimization (PPO) algorithm.

We will apply these components using CARLA version 0.9.8, as it boasts advanced urban simulation capabilities. For a comprehensive understanding, you can find the documented work here.

Prerequisites

Before diving into the setup, ensure you have:

• CARLA (0.9.8) + Additional Maps.
• A supported operating system, preferably Windows or Linux.

Project Setup (Installations)

To set up this project, follow these steps:

1. Clone the repository.
2. Ensure Python v3.7.+ (64bit) is installed.
3. Create a virtual environment:

python -m venv venv

4. Activate the virtual environment:

source venv/Scripts/activate

5. Install dependencies using pip:

pip install -r requirements.txt

6. For additional dependencies, use poetry:

cd poetry
poetry update

7. Start the CARLA server (0.9.8) and ensure you have the additional maps handy.

Finally, start the client:

python continuous_driver.py --exp-name=ppo --train=False

How to Run

Running a Trained Agent

To run the provided pretrained PPO agents for Town 02 and Town 07, you can enter:

python continuous_driver.py --exp-name ppo --train False

To switch to Town 02, simply add:

--town Town02

Training a New Agent

To initiate training for a new agent, use the command:

python continuous_driver.py --exp-name ppo

Like before, you can change towns with:

--town Town02

Understanding with an Analogy

Think of training your autonomous driving agent as teaching a child to ride a bicycle. At first, the child might fall a few times (exploration), but with guidance (rewards for good performance), they learn to balance (behavior optimization). The Proximal Policy Optimization algorithm is like the supportive adult who helps the child make small adjustments in their movements, ensuring they gradually gain confidence and skill, steering them clear of obstacles (like traffic) on their way to mastering the ride.

Troubleshooting

• If CARLA fails to connect, ensure the server is running properly before executing the client.
• For errors concerning dependencies, double-check the installation commands or consider setting up your environment again.
• If you experience performance issues, make sure your hardware meets the required specifications for running CARLA smoothly.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.