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Hands-On Machine Learning with C# by Matt R. Cole

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Reinforcement learning

Reinforcement learning is a case where the machine is trained for a specific outcome with the sole purpose of maximizing efficiency and/or performance. The algorithm is rewarded for making the correct decisions, and penalized for making incorrect ones. Continual training is used to constantly improve performance. The continual learning process means less human intervention. Markov models are an example of reinforcement learning, and self-driving autonomous automobiles are a great example of just such an application. It constantly interacts with its environments, watches for obstacles, speed limits, distance, pedestrians, and so on to (hopefully) make the correct decisions.

Our biggest difference with reinforcement learning ...

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