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Machine Learning, 2nd Edition by Stephen Marsland

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Chapter 11

Reinforcement Learning

Reinforcement learning fills the gap between supervised learning, where the algorithm is trained on the correct answers given in the target data, and unsupervised learning, where the algorithm can only exploit similarities in the data to cluster it. The middle ground is where information is provided about whether or not the answer is correct, but not how to improve it. The reinforcement learner has to try out different strategies and see which work best. That ‘trying out’ of different strategies is just another way of describing search, which was the subject of Chapters 9 and 10. Search is a fundamental part of any reinforcement learner: the algorithm searches over the state space of possible inputs and outputs ...

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