Reinforcement Learning Onramp
This free, two-hour tutorial provides an interactive introduction to reinforcement learning methods for control problems.
Prerequisites: MATLAB Onramp
Access to MATLAB through your web browser
Engaging video tutorials
Hands-on exercises with automated assessments and feedback
Lessons available in English only
Select a Lesson to Get Started
1.
Overview of Reinforcement Learning
Familiarize yourself with reinforcement learning concepts and the course.
- What is reinforcement learning?
- Course overview
- Simulating with a pretrained agent
2.
Defining the Environment
Define how an agent interacts with an environment model.
- Components of a reinforcement learning model
- Defining an environment interface
- Rewards and training
- Including actions in the reward
- Connecting a Simulink® environment to a MATLAB agent
3.
Defining Agents
Create representations of reinforcement learning agents.
- Critics and Q values
- Representing critics for continuous problems
- Creating neural networks
- Actors and critics
- Summary of agents
4.
Training Agents
Use simulation episodes to train an agent.
- Training
- Improving training
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