Advanced Reinforcement Learning: Principles
Machine Learning
| Intermediate
- 11 videos | 1h 12m 56s
- Includes Assessment
- Earns a Badge
This 11-video course delves into machine learning reinforcement learning concepts, including terms used to formulate problems and workflows, prominent use cases and implementation examples, and algorithms. Learners begin the course by examining what reinforcement learning is and the terms used to formulate reinforcement learning problems. Next, look at the differences between machine learning and reinforcement learning by using supervised and unsupervised learning. Explore the capabilities of reinforcement learning, by looking at use cases and implementation examples. Then learners will examine reinforcement learning workflow and reinforcement learning terms; reinforcement learning algorithms and their features; and the Markov Decision Process, its variants, and the steps involved in the algorithm. Take a look at the Markov Reward Process, focusing on value functions for implementing the Markov Reward Process, and also the capabilities of the Markov Decision Process toolbox and the algorithms that are implemented within it. The concluding exercise involves recalling reinforcement learning terms, describing implementation approaches, and listing the Markov Decision Process algorithms.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this courseDefine reinforcement learning and the important terms that are used to formulate reinforcement learning problemsDifferentiate between the implementations of reinforcement and machine learning using supervised and unsupervised learningDescribe the capabilities of reinforcement learning, illustrating its uses cases and example implementationsRecognize reinforcement learning terms that are used in building reinforcement learning workflowsDescribe approaches of implementing reinforcement learning
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Describe reinforcement learning algorithms and their featuresDescribe markov decision process, its variants, and the steps involved the algorithmDescribe markov reward process, with focus on value functions for implementing markov reward processRecognize the capabilities of the markov decision process toolbox and the algorithms that are implemented within itRecall the reinforcement learning terms, describe reinforcement learning implementation approaches, and list the markov decision process algorithms
IN THIS COURSE
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1m 28s
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4m 32sIn this video, you will learn how to define reinforcement learning and the important terms that are used to formulate reinforcement learning problems. FREE ACCESS
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7m 15sIn this video, find out how to differentiate between the implementations of reinforcement and machine learning using supervised and unsupervised learning. FREE ACCESS
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4m 12sUpon completion of this video, you will be able to describe the capabilities of reinforcement learning, and give examples of its uses and implementations. FREE ACCESS
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8m 58sAfter completing this video, you will be able to recognize reinforcement learning terms and use them to build reinforcement learning workflows. FREE ACCESS
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6m 10sUpon completion of this video, you will be able to describe approaches to implementing reinforcement learning. FREE ACCESS
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17m 37sUpon completion of this video, you will be able to describe reinforcement learning algorithms and their features. FREE ACCESS
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10m 27sUpon completion of this video, you will be able to describe a Markov Decision Process, its variants, and the steps involved in the algorithm. FREE ACCESS
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5m 4sAfter completing this video, you will be able to describe the Markov Reward Process, with a focus on value functions for implementing the Markov Reward Process. FREE ACCESS
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5m 41sUpon completion of this video, you will be able to recognize the capabilities of the Markov Decision Process toolbox and the algorithms that it implements. FREE ACCESS
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1m 32sAfter completing this video, you will be able to recall the reinforcement learning terms, describe reinforcement learning implementation approaches, and list the Markov Decision Process algorithms. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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