Machine Learning 简明教程

Machine Learning - Reinforcement

这些方法与之前研究的方法有点不同，并且也很少使用。在这种学习算法中，将有一个代理，我们希望在一段时间内对该代理进行培训，以便它能够与特定环境进行交互。代理将遵循一组与环境交互的策略，然后在观察环境后，它将采取与环境当前状态有关的行动。

These methods are a bit different from previously studied methods and very rarely used also. In this kind of learning algorithms, there would be an agent that we want to train over a period of time so that it can interact with a specific environment. The agent will follow a set of strategies for interacting with the environment and then after observing the environment it will take actions regards the current state of the environment.

以下是强化学习方法涉及的主要步骤：

Here are the major steps involved in reinforcement learning methods −

  1. Step 1 − First, we need to prepare an agent with some initial set of strategies.

  2. Step 2 − Then observe the environment and its current state.

  3. Step 3 − Next, select the optimal policy regards the current state of the environment and perform important action.

  4. Step 4 − Now, the agent can get corresponding reward or penalty as per accordance with the action taken by it in previous step.

  5. Step 5 − Now, we can update the strategies if it is required so.

  6. Step 6 − At last, repeat steps 2-5 until the agent got to learn & adopt the optimal policies.

下图显示了哪种类型的任务适合各种机器学习问题：

The following diagram shows what type of task is appropriate for various ML problems −

type of task