Welcome to the exciting and innovative world of AWS DeepRacer! This guide walks you through how to set up and train a reinforcement learning (RL) model to drive a virtual car around a racing track using AWS RoboMaker. With just a few steps, you’ll understand how to build, simulate, and evaluate your model, making your car a racing champion!

Setting Up Your Environment

Before diving into the fun of racing, you need to lay down some groundwork:

• ROS Kinetic or Melodic: Required for local simulation. Other distributions may work but have not been tested.
• Gazebo: Optional for running simulations locally.
• AWS S3 Bucket: Essential for storing your trained RL model.
• AWS RoboMaker: Required for running simulations and deploying your model.

AWS Account Setup

You’ll need to create an AWS Account and set up your credentials:

• Refer to the AWS Configuration and Credential Files for guidance.
• Create an IAM role with specific permissions as described in the AWS documentation at Creating IAM Policies.

Training the Model

With the setup complete, it’s time to train your model. Think of the training process as baking a cake. You gather your ingredients (environment variables), mix them well, and then let the cake (model) develop its flavor!

Building the Simulation Bundle

In your terminal, use the following commands:

bash
cd simulation_ws 
rosws update
rosdep install --from-paths src --ignore-src -r -y
colcon build
colcon bundle

Running the Simulation

Set the necessary environment variables and kick off your training with:

bash
source simulation_ws/install/setup.sh
roslaunch deepracer_simulation local_training.launch

Your environment variables should include:

• MARKOV_PRESET_FILE: Set to deepracer.py.
• MODEL_S3_BUCKET: Your S3 bucket name.
• WORLD_NAME: Choose your track: easy_track, medium_track, or hard_track.
• And several AWS credentials…

As your model trains, you can monitor the reward function in AWS, which reflects how well your model learns!

Evaluating the Model

After training comes evaluation, which is akin to taste-testing your cake:

Re-use the simulation bundle created during training and run the evaluation using:

bash
source simulation_ws/install/setup.sh 
roslaunch deepracer_simulation evaluation.launch

Troubleshooting

Things might not always go according to plan. Here are some common issues:

• Robot Stalled: If your robot appears to be frozen or spinning, it could be in the second phase of training where it’s optimizing its model without sending new commands.
• Training Doesn’t Seem to Progress: Make sure your hyperparameters are set correctly, and check your environment variable configurations.

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

Conclusion

Congratulations! You’ve learned how to train a reinforcement learning model with AWS DeepRacer. The possibilities with AI and simulation are endless, and we hope this guide has fueled your creative programming endeavors.

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.