Once these different functions (such as the probability distribution) are learned, the correct action to take is simply a matter of deciding which action maximizes the "expected utility" of the agent.

一旦这些不同的函数(例如概率分布)都被掌握，智能体就会很容易判断哪一种行为会让预期效用最大化，并据此选择正确的行为。

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The idea is that the computer (or the "agent") can get a certain amount of value from performing some actions.

它为计算机（或“智能体”，即Agent，译者注）执行一项操作赋予某些分值。

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Reinforcement learning based on Markov decision process is a way of on-line learning, which can be applied to single agent environment.

基于马尔科夫过程的强化学习作为一种在线学习方式，能够很好地应用于单智能体环境中。

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