Introduction to Reinforcement Learning with MATLAB

Watch this video for an introduction to reinforcement learning with MATLAB and Reinforcement Learning Toolbox™. Reinforcement learning is a type of machine learning technique where a computer agent learns to perform a task through repeated trial-and-error interactions with a dynamic environment.

This video introduces reinforcement learning by going through an example that trains a quadruped robot to walk with MATLAB and Reinforcement Learning Toolbox.

You will learn how to:
• Create a reinforcement learning environment in MATLAB and Simulink
• Synthesize reward signals for training
• Create neural network policies interactively or programmatically
• Select and design the appropriate reinforcement learning agent
• Train your agent and inspect training results
• Generate C/C++ code for deploying the trained policy

Learn the essentials of reinforcement learning through Reinforcement Learning Onramp: https://bit.ly/3vUw4ug

Quadruped Robot Locomotion Using DDPG Agent Example: https://bit.ly/3LtYnWN

Related Products:
• Reinforcement Learning Toolbox: https://bit.ly/3vL1Olq
• Deep Learning Toolbox: https://bit.ly/3yEX9TL
• Simulink: https://bit.ly/3vPfNXy

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