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Can you say in short what is “Reinforcement Learning”

Can you say in short what is “Reinforcement Learning”

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Can you say in short what is “Reinforcement Learning”
Reinforcement learning Reinforcement learning occurs when you present the algorithm with examples that lack labels, as in unsupervised learning. However, you can accompany an example with positive or nega- tive feedback according to the solution the algorithm proposes. Reinforcement learning is connected to applications for which the algorithm must make decisions (so the product is prescriptive, not just descriptive, as in unsupervised learning), and the deci- sions bear consequences. In the human world, it is just like learn- ing by trial and error. Errors help you learn because they have a penalty added (cost, loss of time, regret, pain, and so on), teaching you that a certain course of action is less likely to succeed than others. An interesting example of reinforcement learning occurs when computers learn to play video games by themselves. In this case, an application presents the algorithm with examples of specific situations, such as having the gamer stuck in a maze while avoiding an enemy. The application lets the algorithm know the outcome of actions it takes, and learning occurs while trying to avoid what it discovers to be dangerous to pursue survival. You can see how Google DeepMind created a reinforce- ment learning program that plays old Atari's videogames at https://www.youtube.com/watch?v=VleYniJØRnk. When watch- ing the video, notice how the program is initially clumsy and un- skilled but steadily improves with training until it becomes a champion. The process is described as strong and weak points by Raia Hadsell, a senior research scientist on the Deep Learning team at DeepMind, in an enlightening video from TEDx Talks at https://www.youtube.com/watch?v=mqma6GpM7vM. ¿
Transcribed Image Text:Reinforcement learning Reinforcement learning occurs when you present the algorithm with examples that lack labels, as in unsupervised learning. However, you can accompany an example with positive or nega- tive feedback according to the solution the algorithm proposes. Reinforcement learning is connected to applications for which the algorithm must make decisions (so the product is prescriptive, not just descriptive, as in unsupervised learning), and the deci- sions bear consequences. In the human world, it is just like learn- ing by trial and error. Errors help you learn because they have a penalty added (cost, loss of time, regret, pain, and so on), teaching you that a certain course of action is less likely to succeed than others. An interesting example of reinforcement learning occurs when computers learn to play video games by themselves. In this case, an application presents the algorithm with examples of specific situations, such as having the gamer stuck in a maze while avoiding an enemy. The application lets the algorithm know the outcome of actions it takes, and learning occurs while trying to avoid what it discovers to be dangerous to pursue survival. You can see how Google DeepMind created a reinforce- ment learning program that plays old Atari's videogames at https://www.youtube.com/watch?v=VleYniJØRnk. When watch- ing the video, notice how the program is initially clumsy and un- skilled but steadily improves with training until it becomes a champion. The process is described as strong and weak points by Raia Hadsell, a senior research scientist on the Deep Learning team at DeepMind, in an enlightening video from TEDx Talks at https://www.youtube.com/watch?v=mqma6GpM7vM. ¿
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