E4-Unit_2-Introduction_to_Q_Learning-J9-Q_Learning_Recap

中英文对照学习，效果更佳！
原课程链接：https://huggingface.co/deep-rl-course/unit5/conclusion?fw=pt

Q-Learning Recap

Q-学习小结

The Q-Learning is the RL algorithm that :

Q-学习是RL算法，该算法：

• Trains Q-function, an action-value function that contains, as internal memory, a Q-table that contains all the state-action pair values.
• Given a state and action, our Q-function will search into its Q-table the corresponding value.

训练Q函数，这是一个动作值函数，它包含一个包含所有状态-动作对值的Q表作为内部存储器。给定一个状态和动作，我们的Q函数将在它的Q表中搜索相应的值。Q函数

• When the training is done,we have an optimal Q-function, so an optimal Q-table.
• And if we have an optimal Q-function, we
  have an optimal policy,since we know for each state, what is the best action to take.

But, in the beginning, our Q-table is useless since it gives arbitrary value for each state-action pair (most of the time we initialize the Q-table to 0 values). But, as we’ll explore the environment and update our Q-table it will give us better and better approximations

当训练完成时，我们有一个最优的Q-函数，也就是一个最优的Q-表。如果我们有一个最优的Q-函数，我们就有了一个最优的策略，因为我们知道对于每个状态，采取什么是最好的行动。链接值策略。但是，一开始，我们的Q-表是没有用的，因为它给出了每个状态-动作对的任意值。(大多数时候，我们将Q-表初始化为0值)。但是，随着我们继续探索环境并更新我们的Q表，它将给我们越来越好的近似

This is the Q-Learning pseudocode:

Q-learning.jpeg这是Q-Learning伪代码：

Q-学习