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, Galaga.
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.
In this comparison, JARVIS wins.
Pluto kept firing with random movements.
Baymax showed movements recognizing enemies.
JARVIS became much more accurate in movements
and shooting.
JARVIS was better than human beginner and
intermediate.
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.
I trained three types of A.I. with differing
training times.
