Name: 強化学習（RL）環境の構築 - 強化学習 p.4 (Creating A Reinforcement Learning (RL) Environment - Reinforcement Learning p.4)
Uploaded: 2021-01-14T10:36:19.000Z
Duration: 55 min 16 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

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And welcome to part four of the reinforcement learning Siri's in this video we're gonna be doing is building our own cue learning environment.

So the first thing I wanted to do when I learned to learning yes, it was useful to use the Open.

But the first thing I want to do is make my own environment, and I didn't really intend to make a tutorial out of it, but people were asking.

And then it's kind of like, Well, obviously that's what I wanted to do.

So of course, that's what other people probably want to do, too.

So I'm actually just gonna kind of run through the environment that I made and explain it as I go.

So well, we're just gonna do the program linearly.

Ah, but if you have questions, whatever, as always, comment below, join the discord dot gov slash Centex, um, and feel free to ask any questions, but it should be pretty self explanatory, super complicated environment.

And just in case, I guess I'll explain it a little bit before we get in But so, first of all, I love blobs every I used blobs for, like, examples of everything.

So if you haven't noticed by now, um I'm a blob.

A file anyway, So I always wanna make blob things.

And so the idea was to make blobs, and in this case, you've got a player blob of food Blob.

That's the objective we're trying to achieve.

And then just for, uh, you know, complex city, I added an enemy blob as well.

So the idea is that you have this player blob and we'll start by having the enemy and the food not moving, just stay stationary and they just kind of initialize in random positions.

And then the player blob has to move to get to that position, and then later we can add in movement.

It's just that letting the things move is kind of irrelevant to the model.

Learning howto actually move except in one example, which is so if the enemy is moving and you're moving, you, couldn't they remove into each other.

If the enemy is not moving in training, you, you might learn that it's totally fine to get close to the enemy as long as you don't hit the enemy.

But if the enemy can also move, they could collide.

But anyway, not really worried about that.

But you guys can feel for you to tinker with that.

So the first thing that I want to do is open up a command prompt and let's go ahead and pip Install python dash open CVS.

Make sure we have that is open so it might be open C v dash python hoping C v dash Python.

Okay, I feel like I always get that wrong anyway.

Okay, Pip, install open C V dash pine time.

If for whatever reason Python dash opens evey did install for you, you'll probably just installed something really bad.

So, uh, all right, so we are also going to use numb pie, but you should already have that.

And then we're going to use the python imaging library, and I want to say that would be a pip install pillow.

This is a virtual machine, and it should be clean.

So if that's not what I want, Um we'll find out soon enough.

Pillow and then to use pillow you import capital P I L So that should be everything we need.

Let's go ahead and in poor in poor, numb pie as in peace or use numb pie for the array types of stuff from pl we're going to import import how to type image with capital I import CV to We're gonna import Matt plot lib dot pipe lot as p l t.

Plot lived will import style just to make her graph pretty.

And we'll do style that use g pliant, uh, and then finally will import time.

We're unused time purely to set dynamic que table file names.

Probably to underfund 00 it's type of style, huh?

So the first thing I'm gonna do is just try to run this real quick, make sure it actually runs.

Okay, so all our imports worked, and we're gonna get so s.

Okay, so now we're gonna start with some of our constant.

So we're going to say size and we'll say this is 10.

So we're going to skip the whole, make a huge environment and then boil it down to action or observation spaces.

So in this case, it's going to be a 10 by 10 grid.

So the player, the food and the enemy will be initialized at a random location on a 10 by 10.

As time goes on, I'll talk about how some of those changes will impact things.

But obviously, as you increase this size, especially depending on the size of your actions basis, well, that is going to just exponentially explode the number of, ah, you know, possible combinations in your cue table.

How many episodes of H M episodes was A 25,000 were going to say we're going to give a move, Move, underscore Penalty.

So this is if we if we hit the enemy well, it's a it's a 300 so we'll subtract that penalty.

Basically, we're going to say a food reward 25.

This is I haven't really decided where I want this.

I don't know if I really wanted to be 10 like we had with the mountain car.

I don't really know what's the best way to it was the best one to you, so I'm just throwing 25.

Um, we're gonna have capsule line lower case, because it's gonna change over time.

Another thing that we could change a time.

Zero point I went with triple 98 and the way I came into this was I just made a four loop and I literally just decayed.

I'm sorry for, you know, for I and range say 25,000 multiplied this and I just kind of looked and I was like, Okay, that looks good.

So I literally just pulled that number out of nowhere.

I'm gonna say every 3000 episodes, if I recall.

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強化学習（RL）環境の構築 - 強化学習 p.4 (Creating A Reinforcement Learning (RL) Environment - Reinforcement Learning p.4)

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