What Is Reinforcement Learning?
In this video, we provide an overview of reinforcement learning from the perspective of an engineer. Reinforcement learning is type of machine learning that has the potential to solve some really hard control problems.
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
Chapters:
00:00 – Introduction
00:16 – what is reinforcement learning?
03:00 – how does unsupervised learning work?
03:32 – how does supervised learning work?
05:13 – Reinforcement learning environment
05:28 - RL agent
05:58 – RL reward
06:37 – Reinforcement learning policy
07:39 – Reinforcement learning algorithms
09:58 – Exploration vs. exploitation
11:10 Analogies with traditional control design
12:25 – What to consider before formulating an RL problem
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Learn more about Simulink: https://goo.gl/nqnbLe
See What's new in MATLAB and Simulink: https://goo.gl/pgGtod
© 2019 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.
In this video, we provide an overview of reinforcement learning from the perspective of an engineer. Reinforcement learning is type of machine learning that has the potential to solve some really hard control problems.
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
Chapters:
00:00 – Introduction
00:16 – what is reinforcement learning?
03:00 – how does unsupervised learning work?
03:32 – how does supervised learning work?
05:13 – Reinforcement learning environment
05:28 – RL agent
05:58 – RL reward
06:37 – Reinforcement learning policy
07:39 – Reinforcement learning algorithms
09:58 – Exploration vs. exploitation
11:10 Analogies with traditional control design
12:25 – What to consider before formulating an RL problem
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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
© 2019 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.
