Reinforcement Learning
This course provides an overview of reinforcement learning, a type of machine learning that has the potential to solve control system problems that are too difficult to solve with traditional techniques. You'll work through the basics of the reinforcement problem and how it differs from traditional control techniques. You'll also see how neural networks are used to represent unknown functions and how the agent uses rewards from the environment to train them.
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- Level Professional
- Course by MathWorks
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About
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI). Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end. By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science. The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more.Modules
Untitled Lesson
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7
Videos
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1
Assignment
1
Assignment
- Knowledge Check
7
Videos
- What is Reinforcement Learning?
- Understanding Reinforcement Learning Environment and Rewards
- Reinforcement Learning Policies and Learning Algorithms
- Solving the Walking Robot Problem with Reinforcement Learning
- Overcoming the Practical Challenges when using Reinforcement Learning
- What is Multi-Agent Reinforcement Learning
- Why Choose Model-Based Reinforcement Learning?
Auto Summary
Explore the power of adaptive learning systems and AI with the Reinforcement Learning Specialization. This 4-course series, taught by experts from the University of Alberta, delves into solving real-world problems using trial-and-error interaction. Perfect for professionals aiming to understand modern probabilistic AI, the course covers small-scale problems and practical applications in various industries. Available on Coursera with Starter and Professional subscription options.
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