16.12 Reinforcement Learning
Reinforcement learning is a form of machine learning in which algorithms learn from their environment, similar to how humans learn—for example, a video game enthusiast learning a new game, or a baby learning to walk or recognize its parents.
The algorithm implements an agent that learns by trying to perform a task, receiving feedback about success or failure, making adjustments then trying again. The goal is to maximize the reward. The agent receives a positive reward for doing a right thing and a negative reward (that is, a punishment) for doing a wrong thing. The agent uses this information to determine the next action to perform and must try to maximize the reward.
Reinforcement learning was used in some key artificial-intelligence ...
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