Reinforcement Learning: From Zero to State of the Art with Pytorch 4
This article explains the basics of reinforcement learning, including its components and the different types of algorithms.
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This article is about reinforcement learning, which is a type of machine learning. In reinforcement learning, a computer learns how to complete tasks by trial and error. The computer is rewarded for making correct decisions, and it is punished for making incorrect decisions. The goal of reinforcement learning is to find the best way to complete a task.
Reinforcement learning can be used to solve complex problems that are difficult for humans to solve. For example, it can be used to develop robots that can move and interact with their environment. It can also be used to develop computer programs that can play video games.
Reinforcement learning is different from other types of machine learning because it focuses on learning from experience. The computer is given a set of rules, and it must learn how to complete a task by trial and error. This makes reinforcement learning an effective way of solving complex problems.
Reinforcement learning has become an important part of artificial intelligence research. It is being used to develop robots and computer programs that can solve complex problems. It is also being used to develop better video games and to improve the performance of existing algorithms.
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