Reinforcement Learning (RL) is an advanced machine learning (ML) technique that learns very complex behaviors without requiring any labeled training data, and can make short term decisions while optimizing for a longer term goal.

You can use the AWS RoboMaker sample application to generate simulated training data used for RL. The RL model will teach the robot to track and follow an object. This is a simple demonstration that can be extended into use cases like worker assistance in a warehouse or an entertainment robot following a consumer in their home.

In this project, you will go through the steps to build a robotics application. This application will use reinforcement learning to train a robot (TurtleBot 3 Waffle Pi is used as an example) to drive autonomously towards a stationary robot (TurtleBot 3 Burger is used as an example). You will learn to train and evaluate the reinforcement learning model in AWS RoboMaker simulation and deploy the model to a physical robot using AWS RoboMaker fleet management.

The learning path consists of the following steps: