Why Choose Model-Based Reinforcement Learning?

What is the difference between model-free and model-based reinforcement learning? Does training against a simulation model require model-based reinforcement learning? What if you don’t have a model available? Explore the differences and results as the learning models are applied to balancing a cart/pole system as an example. By the end, you will have a better understanding of situations where you may want to choose model-based reinforcement learning. MATLAB Example: Train MBPO Agent to Balance Cart-Pole System: https://bit.ly/3PEzwBi Watch this video to see how to apply model-based reinforcement learning with Reinforcement Learning Toolbox: https://www.youtube.com/watch?v=_rFS0FzJkgk Watch our full video series about Reinforcement Learning: https://youtube.com/playlist?list=PLn8PRpmsu08qw_IwpgVNsKiJQpvvW0MmM By the end of this series, you’ll be better prepared to answer questions like: - What is reinforcement learning and why should I consider it when solving my control problem? - How do I set up and solve the reinforcement learning problem? - What are some of the benefits and drawbacks of reinforcement learning compared to a traditional controls approach? Artificial intelligence, machine learning, deep neural networks. These are terms that can spark your imagination of a future where robots are thinking and evolving creatures. Check out these other resources: - Reinforcement Learning by Sutton and Barto: http://bit.ly/2HAYbb4 - Reinforcement Learning Course by David Silver: https://youtu.be/2pWv7GOvuf0 - Reinforcement Learning Toolbox: https://bit.ly/2YjuAYa - Deep Reinforcement Learning for Walking Robots: https://www.youtube.com/watch?v=6DL5M9b2j6I Check out the individual videos in the series: • What Is Reinforcement Learning?: https://youtu.be/pc-H4vyg2L4 • Understanding the Environment and Rewards: https://youtu.be/0ODB_DvMiDI • Policies and Learning Algorithms: https://youtu.be/7cF3VzP5EDI • The Walking Robot Problem: https://youtu.be/Wypc1a-1ZYA • Overcoming the Practical Challenges: https://youtu.be/zHV3UcH-nr0 • An Introduction to Multi-Agent Reinforcement Learning: https://youtu.be/qgb0gyrpiGk • Why Choose Model-Based Reinforcement Learning?: https://youtu.be/ztT2ZLWTfXw 0:00 Introduction 0:28 What is model-free reinforcement learning? 1:13 What is model-based reinforcement learning? 1:44 A sports analogy of model-free vs model-based reinforcement learning 3:25 Real vs simulated experiences 4:59 Using simulation environments does not imply model-based reinforcement learning 5:50 Why choose model-free reinforcement learning? 6:50 Why choose model-based reinforcement learning? 8:29 What if you don’t have a model? 10:25 Learning an ensemble of models 12:54 Model-based reinforcement learning example in MATLAB -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2022 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.