Hello, welcome to A.I.
Playpen.
Humans learn games by looking at the screen.
Very similarly, recently developed artificial
intelligence can learn to play games, by only
looking at the screen.
In this video, the goal is to see how well
AI learns to play the game, Bubble Bobble.
I compare one human beginner team, one human
intermediate, and four artificial intelligence
agents with differing expertises.
I named these AIs using the character names
in animations and movies.
I'll call them Pluto, Baymax, Jarvis, and
T-800.
Overall, the AIs preferred direct kiss attacks
than the complicated procedure to trap enemies
in the bubble first and pop them up.
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.
Thanks for watching.
In this comparison, human intermediate wins.
Pluto was not good.
Baymax recognized enemy positions a little.
JARVIS was good at “kiss attack”.
T-800 was even more accurate.
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 four types of A.I. with differing
training times.
