使用强化学习解决分类问题 [英] Using Reinforcement Learning for Classfication Problems

问题描述

我可以在分类中使用强化学习吗？如人类活动识别？

Can I use reinforcement learning on classification? Such as human activity recognition? And how?

推荐答案

有两种类型的反馈。一种是评价性，用于强化学习方法，另一种是说明性，用于主要用于分类问题的监督学习。

There are two types of feedback. One is evaluative that is used in reinforcement learning method and second is instructive that is used in supervised learning mostly used for classification problems.

使用监督学习时，将基于训练数据集中提供的正确标签的信息来调整神经网络的权重。因此，在选择错误的类别时，损失会增加，权重也会随之调整，因此对于这种输入，就不会再次选择该错误的类别。

When supervised learning is used, the weights of the neural network are adjusted based on the information of the correct labels provided in the training dataset. So, on selecting a wrong class, the loss increases and weights are adjusted, so that for the input of that kind, this wrong class is not chosen again.

但是，在强化学习中，系统探索所有可能的动作，在这种情况下为各种输入分类标签，并通过评估奖励来确定对与错。也可能是这样，直到获得正确的类标签，它可能会给出错误的类名称，因为这是迄今为止找到的最好的输出。因此，它没有利用我们对班级标签所掌握的特定知识，因此与监督学习相比，会大大降低收敛速度。

However, in reinforcement learning, the system explores all the possible actions, class labels for various inputs in this case and by evaluating the reward it decides what is right and what is wrong. It may be the case too that until it gets the correct class label it may be giving wrong class name as it is the best possible output it has found till now. So, it doesn't make use of the specific knowledge we have about the class labels, hence slows the convergence rate significantly as compared to supervised learning.

您可以对分类问题使用强化学习，但这不会给您带来任何额外的好处，反而会降低收敛速度。

You can use reinforcement learning for classification problems but it won't be giving you any added benefit and instead slow down your convergence rate.

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