This one-day course introduces reinforcement learning in the MATLAB® and Simulink® environments, focusing on using the Reinforcement Learning Toolbox™.
- Environment and Rewards
- Policy and Agent
- Neural Networks and Training
Day 1 of 1
Environment and Rewards
Objective: Set up an environment and shape rewards in Simulink or MATLAB.
- Set up environment in Simulink
- Write a reward function
- Set up an agent using Simulink and MATLAB
- Connect agent and environment
Policy and Agent
Objective: Create an policy representation and construct an agent.
- Represent a policy with a neural network
- Create a reinforcement learning agent in MATLAB
- Specify simulation options to run a simulation
Neural Networks and Training
Objective: Assemble a neural network for a policy representation and train an agent.
- Assemble a neural network
- Deep Network Designer app
- Training an agent
- Reinforcement Learning Designer app
Objective: Generate code from a trained agent.
- Generate code
- Validation of code