Positive Reinforcement:

積極加固：

When the agent completes any task, if the feedback or the points for the task are in a positive response, then it is termed as the positive reinforcement. This type of reinforcement increases the performance of the agent as the agent now gets a hint that it has to make decisions and perform tasks in this particular manner to earn more rewards in the future also.

當代理完成任何任務時，如果任務的反饋或要點處于積極響應中，則稱為積極強化。 這種增強方式可以提高代理的性能，因為代理現在可以暗示它必須以這種特定方式做出決定并執行任務，以在將來也獲得更多的回報。

Negative Reinforcement:

負加固：

Whenever the agent fails to perform any task as required, in that case, the agent is provided with negative reinforcement. This can be thought as of giving punishment to a child for doing mischiefs. The negative reinforcements tell the agent that such type of performance or such type of decisions must be avoided in the future while solving similar types of problems.

每當代理未能按要求執行任何任務時，在這種情況下，就會為代理提供負加固。 可以認為這是對孩子作惡的懲罰。 負面的補充告訴代理人，將來在解決類似類型的問題時，必須避免這種績效或這種決策。

Input:

輸入：

The Agent seeks the initial stage as the input from which it has to start. This is an important phase because all the observations and inferences will be drawn starting from this state, and the past state of the agent will not be considered.

代理尋求初始階段作為必須從其開始的輸入。 這是重要的階段，因為將從此狀態開始繪制所有觀察和推論，并且不會考慮代理的過去狀態。

Output:

輸出：

The output state that the system will reach after solving a certain problem is not fixed as there are multiple ways of solving a problem and the agent can choose different solution whenever it tries to solve the same type of problem.

系統解決某個問題后將達到的輸出狀態不是固定的，因為有多種解決方法，并且座席在嘗試解決同一類型的問題時可以選擇不同的解決方案。

Training/Learning:

培訓/學習：

The training phase or the Learning Phase is when the agent builds its Knowledge Base from the reward or punishment that it gets based on the output it produces. It is a very important phase in Reinforcement Learning because it helps the agent to understand and learn in the same way as humans. This implements the human behavior in agents which is the main target in Artificial Intelligence.

培訓階段或學習階段是指代理根據其產生的輸出所獲得的獎勵或懲罰建立其知識庫。 這是強化學習中非常重要的階段，因為它可以幫助代理以與人類相同的方式來理解和學習。 這在代理中實現了人類行為，而代理是人工智能的主要目標。