강화 학습과 의사 결정의 이해

강력한 훈련 동작을 수행하려면 OVD비전의 프로세스를 이해하는 것이 중요합니다. 강화 학습에서 중요한 동작 전환 상태.

### 의사 결정에서의 강화 학습과 확률 분포
* 🤖  이 글에서는 강화 학습의 의사 결정 과정과 확률 분포를 이해하는 것의 중요성에 대해 설명합니다.
* 🎲  특정 상태에서 조치를 취하고 새로운 상태로 전환하는 맥락에서 확률 분포의 개념을 설명합니다.
* 📊  또한 국가의 행동 분포를 결정하는 정책과 강화 학습에서 올바른 행동을 찾는 것의 중요성에 대해서도 다룹니다.
* 💡  엡실론 수익률, 내재값, 평균 수익률과 같은 용어를 소개하며 강화 학습에서 평균값을 찾는다는 목표를 강조합니다.
* ♟️  궁극적인 목표는 가장 보람 있는 행동을 찾아 수익을 극대화하여 다양한 시나리오에서 효과적인 의사결정을 내리는 것입니다.

Do not think like this. If Ace One is given, one leg is given, right? If one leg is given, we don't need the other leg. So, if they provide me with the information, right? Yes. Then, there are many things to tell you, and about the process of OVDision. Actually, it's always better to know the process that comes out while doing strong training. In fact, I have been studying a lot without knowing this, but eventually, I had to study about the process that needs to be changed. But it's not a big deal, right? So, if you see, there are still things left. What is the essence of this? It's actions, actions. Actions are continuously carried out. So, processes, users, they progress. That's why Precept Maco's anxiety talk starts from why. Let's make a point first. Let's talk about Yessu Zero, which is a little bit of a state. Now, it's a bit confusing, but let's express it.

This is how it is, right? Starting with a single baby, s0 is the starting state. If you look from the opposite side, it is the theme of the chicken Rudia to cook it red. Here, s2 and s3 are open. It can be done here, right? N20, what is it? It can be possible, but the starting point is subtracted from the s value. It is determined here, right? There may be many starting points depending on your situation. In some cases, this can lead to starting such things. Then, depending on your situation, you may have different starting points. What should be here is determined by the value of s1, which may be 0 depending on the situation. What is needed here may be different. Maybe it's unconstitutional once or twice a year. It can vary like this. So, it represents the starting street more. In other words, it means that your birthday will be 10/1. It can be this value, and then you take action from there. If you take action from the state called s0, what do you call it? Let's call it a0.

Let's go to a0, then tie them up, tie them up, s0, and end. So what happens if you do a0 action? Then let's go straight to s0 estate, a0, and then to the nation. Then, what happens to the state? If you look here, whether you go down from the starting point, go to the right, go up, or go to the left, whatever direction you go, it will be the same. But the results will be different. In any case, the important thing is that when you do a0 in front of s, it transitions to s1. Transitioning from what is tied up now to s1 is important. It's crucial because it's in a critical state now. That's why sons are also a1. What happens when you do a1 from s1? It will surpass the burden.

In s2, you will repeat actions like this. In s2, you will support sd actions, move on to ghost stories, and beyond. There is a characteristic that is considered important in the DC process. If you remember this illustration a bit, there are properties that are satisfied in that illustration. I will talk about those properties. The first importance is that, first of all, this is about probability. Half of it becomes a conditional probability. First of all, when talking about digital processes, the important point is that everything is rendered. So, having some randomness, having a random barrier, means talking about PDF. If you have a container, when you call a container slam dumbbell, it will appear as a PDF.

Let's call it a1 here. This means that it is given in section s and also given in section a, and the sweat is given in section s. Yes, this is not a nonsense. Because in the situation of the Ant Cell, the role of our West Sea regime is not knowing the action. KAIST today is too correct, right? In this situation, given strangely in s0 as s1, the probability of a1, a random consideration, is how it is. What does it mean to erase it? It means that most drawings are transferred from s0 to s1, right? Then, in order to represent a1, which is given in a strange way, how does the probability of a1, which is given in s1, transition from aa to s? Then, in order to represent a1, which is given in a strange way, how does the probability of a1, which is given in s1, transition from aa to s? Then, in order to represent a1, which is given in a strange way, how does the probability of a1, which is given in s1, transition from aa to s? Then, in order to represent a1, which is given in a strange way, how does the probability of a1, which is given in s1, transition from aa to s? Then, in order to represent a1, which is given in a strange way, how does the probability of a1

It adds interest to the a1 from s1, which is now popping up in a line just before. It's like, hak-hak, what's a resignation letter just before? So, if a blue resignation letter is given, others are not needed. That's the content. If you look at it, you should think like this. It talks about dissing Ace1. Even if you forget Ace1, there is this... this leg is dipped, and this leg is crossed. Two feet are not necessary, only one leg is needed. Because... because if they are given as information in Service SL, they need to go beyond s1. So, you should have all the information about Ace1 and the app, and the professor is there, not me, but if you are close, you have already decided about a1. It was decided. One month, two months, and these guys are not needed, so after two months, these guys are not needed. What future guys write... Future guys write one month again, so they don't know more. That's it.

It could also have an impact, so the kids in Canada are given, but they don't know that. When you cross the two legs, they seem to have found Iron expands. Let's talk about the probability of the second one, the second one about 4 seconds. Let's say it's given like this. Yeah, now get ready, use Lance Zero for work, and use it a lot. So, it's the same as working at Health One. So today, I couldn't do it, so I asked for help. If that's the case, how will the probability of L's 2 be conveyed? When I asked that, it seems like it will be gathered here. First of all, when you look at cream and bragging, can I fill in s0 a0 because it's far away? Since you're far away, can you do it step by step? How about s1? Can you erase Annie or how can it be erased? It seems like it can be erased. What has been set up in s1 must be done. You have to do what's set up in s1 while working.

Therefore, right now, in this picture, there should be two circles appropriately placed here so that we can move on to the next step. This determines the decision without any heat connected in a single line, and then when there is s1 or sa1, I make the decision to overtake the burden from me. That's why all bridges are paying, what they receive in a month with a university in a month. Now, this choice is made to enter, so it's like joining this choice. It's a month of y, not a month of s1. It's not like that, it's a circle or a month, so because it's a month, there should be one. These guys need to have one. The probability of a significant change in the disappearance of the organization's line is that the information of these two will be found, so they are exceptions. There is a small number of exceptions. There are two legs on the other side. You can live now. Here, because s is zero, you can live because you have one leg, even though the two legs are apart, but they are still two months or catch.

In total, there are two decks. If there is no red card on this side, there is no information, and there is a possibility of a total of two meetings. It is not really necessary on this side, and it will be a little easier to cross both legs, as all the information is available and can be shared. As I mentioned earlier, it's related to the guild strength. Now, an important keyword comes out here. The 's1' child is already erased, and 's1 11' states a certain probability distribution. 'A-on' is a certain probability distribution. So, this is like up, down, left, right, with a red glass. It's a type of time and a type of this type, and there's a probability for one side. So, for example, on the right, the probability is the highest, and the rest is zero.

It will be like this. If you search greedily, you're in the middle of going up this blood. This zeta that came up to the upper right is the probability distribution PMF, so if you make it into PMP, it becomes a bargaining ration. In this situation, we call that example a policy. It holds that the distribution of what action to take from that state is precisely the policy. How to act, that's what we need to figure out. What action to take from S1, what a great ability it is, right? So, this is a conversation for the future, about what timing, for what time, for what state, about all of its actions, is precisely the policy. And we are going through this process well, finding out the actions. Earlier, the action was correct, in the design process, it's DC. It's just about finding out the actions. What determines our members directly is precisely this policy, the plan for what actions to take.

Vertically, the action bar state is honey. So, what are these terms? What is a squat pattern? What is a syron 2? Becoming familiar with these terms is to accurately convey a little more precisely. It is a bit intuitive and also accurate. Yes, what did you say earlier about mdb? Is it better than mdp slr? I don't know in reinforcement learning. I think a lot of rewards are given. What does this mean? In fact, if you look a little closer, it's not exactly maximizing the return. A bit more precisely, there is the existence of an epsilon return. Maximizing that existence is what comes to mind when it comes to the goal of reinforcement learning. So, what is l? It can be interpreted as an intrinsic value. Let's try to pack that. This means something in the context of reinforcement learning. It means 1m in the past or linked to 2 cups of pensions.

Metaphors representing justice are often used. Remember the fatigue, we talked about the agreement between Lea and Diabina, so the discount is finally available. The flow of the U.S. is received at that time, but where is it? However, only the real data received next, and the sense of unity received next, is not yet clear. When you take an action called 'at,' you will aim for the goal of knowing when to do it. When you move on to doing 'at' and 'nation,' you will receive rewards, so it is often expressed as 'rt plus one.' I need to know the actions 'h' to receive the rewards when I take the action 'at.' It will be convenient like this. So, it seems more comfortable in terms of conditions or numbers. Therefore, in fact, there is no 'at' action, so you have to express it as 'rt plus' when you move on to the next state after taking the action 'at.' Then it will be 'rt plus,' it will be like 2+2, right? It's just a difference in rotation, that's why.

If you look, what is the return now, you've already earned money here, if you look here, you'll know what happened. But there are a lot of newbies here, and you have to proceed with the Rio that you met here around midday. You have to proceed once, so it's attached to gamma. But the reward Yeongi has no reason now. It's the same here. It's the third time it's been empty, so if you expect him to be a hero, it's zero, and gamma ig plus 2 is a lot, so it will improve the return on the right side. The important thing is why the cow is gamma, why can it be returned, and why h is being said now. Even if you try h here, it doesn't matter, it doesn't continue, and there's no medical treatment. This can be obtained next, so the value of the guess about that becomes m. The fact of this education is that I'm more scared than people. When this is done, the first word is from the 5th, and there is a child, and the action.

States have all been seen randomly. So, when it comes to Sean Estate, I think rewards are also random. Sean Estate has a lot of movement, so there is no fixed point. That's why people think that DotA is random. That's why these sports have existed. But what we're talking about is taking the average value. The goal is to take the average return. It's about finding the average value. That's what we're aiming for. The more you catch, the more actions you find. In other words, you end up finding places that like matches a lot. This user's book will have some missions. Will you maximize internal maximizer? That's what we're looking for. Exactly, you have to write two. Do you understand? The goal is not to keep the data of the estate here.

The goal is to find a flea seed that I want at a certain time, so I'll send it right away. So, if you only have this, how should I act? It hasn't all come out yet. In this situation, in a situation like a chessboard, you play like this. Simon also comes out crying. That's right. Among the names to find, what you're looking for is going, but what is Jet B for everyone? The respect editor does not do the maximum, so it is there. That's why it's not there, it's done.

[강화학습] 2-1강. Markov Decision Process (MDP) 쉬운 설명

AI 강의 / 딥러닝 강의 등
최고의 설명법을 연구합니다.

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혁펜하임 | AI & 딥러닝 강의

OVD비전의 프로세스와 액션의 중요성

OVD비전은 일련의 연속적인 동작과 상태 간 전환을 포함하며, 시작점은 s0으로 표시됩니다.

각 동작이 다른 상태와 결과로 이어지기 때문에 이 과정이 매우 중요하며, 이는 OVDision에서 동작의 중요성을 강조합니다.

한 상태에서 다른 상태(예: s0에서 s1로)로 전환하는 것은 프로세스의 중요한 측면이며, 궁극적으로 각 작업의 결과를 결정합니다.

디지털 프로세스에서 확률의 중요성

확률은 디지털 프로세스, 특히 한 상태에서 다른 상태로 전환할 때 중요한 역할을 합니다.

이러한 맥락에서 조건부 확률의 개념은 중요합니다.

디지털 프로세스에서 무작위 장벽을 렌더링하는 것은 확률 밀도 함수(PDF)의 개념 및 서로 다른 상태 간의 확률 전환과 밀접한 관련이 있습니다.

L의 2 확률과 의사 결정의 영향력

두 다리를 교차하면 철이 팽창하여 의사 결정에 영향을 미칠 수 있는 것으로 밝혀졌습니다.

L의 2 확률은 원의 배열과 확률 분포에 대한 "A-on" 개념의 영향을 받습니다.

제공된 정보는 길드의 힘, 의사 결정, 중요한 변화의 확률 사이의 관계를 암시합니다.

강화 학습의 정책은 주어진 상태에서 취할 행동의 확률 분포를 결정합니다.

목표는 각 상태에서 어떻게 행동해야 하는지 파악하여 궁극적으로 수익을 극대화하는 것입니다.

이 과정에서 엡실론 수익의 존재는 장기 보상을 극대화하려는 강화 학습의 목표에 있어 매우 중요합니다.

정의의 혼란스러운 은유와 행동

레아와 디아비나 사이의 할인 계약에 대한 언급과 함께 'at'과 '국가'에서 행동을 취하는 'RT 플러스 원'의 개념에 대한 언급이 있습니다.

또한 보상을 받는 데 있어 'h' 행동의 중요성과 수익률 향상에 있어 감마의 역할에 대해서도 설명합니다.

전반적으로 이 구절은 특정 행동을 취하는 것의 복잡성과 그것이 보상에 미치는 영향을 은유적인 방식으로 다루고 있는 것 같습니다.

DotA에서 션 에스테이트의 무작위성

DotA의 션 에스테이트는 무작위 보상과 많은 움직임으로 인해 게임에서 무작위성이라는 인식을 불러일으키는 것이 특징입니다.

그러나 주요 목표는 평균 수익을 계산하고 무작위성 속에서 평균값을 찾는 것입니다.

부동산 데이터를 추적하는 것보다 내부 가치를 극대화하는 데 중점을 둡니다.