人工智能ai 学习

Learning Agents as described earlier are the systems which are capable of training themselves by learning from their own actions and experiences. The Learning process in the agent is broadly classified into three types:

如前所述， 学习代理是能够通过从自己的行为和经验中学习来进行自我训练的系统。 代理中的学习过程大致分为三种类型：

1. Supervised Learning

   监督学习

2. Unsupervised Learning

   无监督学习

3. Reinforcement Learning

   强化学习

1)监督学习 (1) Supervised Learning)

As the name itself suggests, in this type of learning, the agent is supervised in every means in prior itself. What it simply means is that the correct answer for almost each example problem is fed in the Knowledge Base of the system initially in its development phase. SO, whenever the agent confronts ay problem, it tries to find the same problem or a similar problem in its knowledge base whose solution it already has embedded in its system. If the problem is not there or is a lot different from those already residing in its system, then in those cases, the agent fails to function or perform any necessary action.

顾名思义，在这种类型的学习中，对代理人进行事先各种方式的监督。 它的简单含义是，几乎每个示例问题的正确答案最初都是在系统的开发阶段就输入到系统的知识库中。 因此，每当代理遇到问题时，它都会尝试在其解决方案已嵌入其系统中的知识库中找到相同或相似的问题。 如果问题不存在或与系统中已经存在的问题有很大不同，则在这种情况下，代理将无法运行或无法执行任何必要的操作。

2)无监督学习 (2) Unsupervised Learning)

In the unsupervised learning agents, the answers to the problems are not available with the agent in advance. In this type of learning, the agent has to itself find the solution to the problem by learning from its past actions and experiences. However, the required information which forms the foundation of the Knowledge Base is provided to the agent in its development phase, but it has to find the solutions by itself. This type of agent is smarter than the Supervised Learning agent as it has the ability to find a relevant solution to those problems also which the agent have faced for the first time and has no prior knowledge or experience regarding it.

在无人监督的学习代理中，无法通过代理提前获得问题的答案。 在这种类型的学习中，主体必须通过从过去的行为和经验中学习来自己找到问题的解决方案。 但是，构成知识库基础的所需信息在其开发阶段已提供给代理，但它必须自行找到解决方案。 这种类型的代理比“监督学习”代理更聪明，因为它能够找到与这些问题有关的解决方案，而这些问题也是该代理首次遇到的，并且没有相关的先验知识或经验。

3)强化学习 (3) Reinforcement Learning)

In the Reinforcement Learning method, the learning process is almost the same as in Unsupervised learning. But the difference is that, in Reinforcement Learning, the agent is given some reward occasionally for completing any task. Here, the goal of the agent is to get the maximum of such rewards. So, whenever any agent tries to find the solution to any problem, it searches for an alternative which would give him the maximum reward points. This type of learning not only makes the agent smart but also helps it to take the best possible decision according to the utility of the developer or the user. The utility based agents use this type of learning in their systems.

在强化学习方法中，学习过程与无监督学习中的学习过程几乎相同。 但是不同之处在于，在强化学习中，代理会因完成任何任务而偶尔获得一些奖励。 在这里，代理商的目标是获得最大的这种回报。 因此，无论何时任何代理人试图找到解决任何问题的方法，它都会寻找一个替代方案，该方案将给他最大的奖励积分。 这种类型的学习不仅使代理变得聪明，而且还可以根据开发人员或用户的效用帮助其做出最佳决策。 基于实用程序的代理在其系统中使用这种类型的学习。

翻译自: https://www.includehelp.com/ml-ai/types-of-learning-in-agents-in-artificial-intelligence.aspx

人工智能ai 学习