Learn the basics of creating intelligent controllers that learn from experience in MATLABĀ®. Add a reinforcement learning agent to a SimulinkĀ® model and use MATLAB to train it to choose the best action in a given situation.

Familiarize yourself with reinforcement learning concepts and the course.

Lessons:

- What is Reinforcement Learning

- Simulating with a Pretrained Agent

Define how an agent interacts with an environment model.

Lessons:

- Components of a Reinforcement Learning Model

- Defining an Environment Interface

- Providing Rewards

- Including Actions in the Reward

- Connecting a Simulink Environment to a MATLAB Agent

Create representations of RL agents.

Lessons:

- Critics and Q Values

- Representing Critics with Neural Networks

- Actors and Critics

- Summary of Agents

Use simulation episodes to train an agent.

Lessons:

- Training

- Changing Options

- Improving Training

Learn next steps and give feedback on the course.

Lessons:

- Review of the RL workflow

- Additional Resources

- Survey

Get started quickly using deep learning methods to perform image recognition.

Get started quickly with the basics of Simulink.

Get started quickly with the basics of MATLAB.