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Practical Machine Learning Cookbook by Atul Tripathi

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An overview of reinforcement learning

Reinforcement learning is the problem of getting an agent to act in the world so as to maximize its rewards. It is about what to do and how to map situations to actions so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. The two most important distinguishing features of reinforcement learning are trial and error and search and delayed reward. Some examples of reinforcement learning are as follows:

  • A chess player making a move, the choice is informed both by planning anticipating possible replies and counter replies.
  • An adaptive controller adjusts parameters ...

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