Hello, welcome to A.I.
Playpen.
I show how well the AI plays retro games,
and compare them to humans.
In this video, the goal is to see how well
AI learns to play the game, Double Dragon.
I compare one human beginner, one human intermediate,
and three artificial intelligence agents with
differing expertises.
I named these AIs using the character names
in animations and movies.
I'll call them Pluto, Baymax, and Jarvis.
Thanks for watching this video.
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Also, please leave a comment about your favorite
retro game.
If it is doable, I will try to train AI to
see how well AI plays your favorite game.
I trained the A.I. with reinforcement learning.
I used the proximal policy optimization algorithm.
The network was the standard convolutional
neural network with 3 internal layers.
I used the OpenAI baselines toolkit, and the
OpenAI retro environment.
The data inputs were the stream of 2D pixel
images and rewards.
In this comparison, human intermediate wins.
Double Dragon has some situations that the
player has to enter the door or scroll the
environment.
For human, it was natural to do those things.
However, the AI had a hard time in doing what
is supposed to do in those situations.
