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Reinforcement Learning Onramp

MathWorks via MATLAB Academy


  • Overview of Reinforcement Learning: Familiarize yourself with reinforcement learning concepts and the course.
  • Defining the Environment: Define how an agent interacts with an environment model.
  • Defining Agents: Create representations of RL agents.
  • Training Agents: Use simulation episodes to train an agent.
  • Conclusion: Learn next steps and give feedback on the course.


  • What is Reinforcement Learning
  • Simulating with a Pretrained Agent
  • 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
  • Critics and Q Values
  • Representing Critics for Continuous Problems
  • Creating Neural Networks
  • Creating Networks for Agents
  • Actors and Critics
  • Creating Default Agent Representations
  • Summary of Agents
  • Training
  • Changing Options
  • Improving Training
  • Summary of Functions
  • Additional Resources
  • Survey

Taught by

Matt Tearle


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