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Playing a video game is more than mere procrastination | BMC Psychology | Full Text
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Search Search Search Browse menu. Sign in. Dino Lingo See all. Back to main collection Sign in. I briefly mentioned that Reinforcement Learning can be used to help an AI train itself, but how does it actually work? In RL, the AI trains on a reward based system. Think back to elementary school. Every time you did something right, your teacher might give you a cookie or a sticker.
This would encourage you to do more similar actions because you want as many cookies and stickers as you can get. Every time you did something wrong you might get a timeout or lose recess, which would discourage you from doing that again. The agent learns in a very similar way. We set conditions that if the AI follows correctly, they will receive an imaginary reward.
If they do the wrong things they lose these rewards. When we apply this to Space Invaders, the agent extracts observations from the environment which will help it decide the right action to take. For example, if it sees a bullet coming towards it, it will move out of the way! How is it able to recognize that a bullet is coming towards it or even what a bullet is?http://designyourlocket.com/components/86/4580-love-compatibility.php
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It is able to do all of these things with the use of a Neural Network. Neural Networks NN are modeled after the human brain.
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In a standard NN, there are three basic types of layers: an input layer, hidden layers, and an output layer. The input layer takes the input, which is processed by the hidden layers, and finally spit out the output in the output layer. What makes CNNs unique is their ability to analyze imagery. They contain convolutional layers which take images as inputs and sweep over them pixel by pixel. Each convolutional layer is designed like a filter, searching for something specific in the image. You can have layers searching for something as general as a square to something more complicated like a bird!
Okay, so our AI can tell what is what in our environment. Which way is it going?
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How fast is it going? Will we be able to go across without getting hit? The last thing we need to do so that our AI can properly understand the environment is stack images to add directional sense.
If we take 1 image, wait a couple frames, take another image, and so on, we are able to create something similar to a GIF for our AI to perceive. At this point we are giving the AI a reward for shooting the Aliens, the AI knows how to understand the environment, but there is still one fundamental component we are missing. How does the AI know what to do in what situation? If you or I were to play the game, we would have a general idea of what to do.
Shoot the aliens and dodge the bullets, simple! But how did we know to do this?
The problem is, the AI has no past experiences. Think of it like an infant who has the ability to play video games. So what better way to learn how to play a game then by playing the game! So the AI will first create something called a Q-Table. A Q-Table contains a column for every possible action in your game, and a row for every possible state in your game.