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

Format:Self-paced

Language:English

- Hands-on exercises with automated feedback
- Access to MATLAB through your web browser
- Shareable progress report and course certificate

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.