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Infinite Mario AI - Long Level

Robin Baumgarten · 1.3M views · 2009-08-05 · 1:11

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Overview

A bot built by Robin Baumgarten plays Infinite Mario using A* path-finding, shown as a red planned path that updates as new obstacles appear. It won the Mario AI competitions and uses only on-screen information.

On Hacker News

HN points to it as a vivid demo that plain A* search can beat fancier machine-learning approaches in game AI.

Recommended by 3 HN people across 3 months · HN mentions 2009–2012

Topics

Chess, board games & puzzlesAlgorithms & data structuresMachine learning & deep learning
Search by tags
a-starmario aigame aipath planningcompetition

What people said

JonnieCache · 2011-07 · Recommendation

The original java infinite mario was used in the fascinating Mario AI competition. Details and papers here: [LINK] Video of last years winning entry at work here: [VIDEO] It's pretty interesting, a lot of people wrote/are writing high falutin' machine learning algorithms, but they are consistently trounced by good old A* techniques.

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feral · 2010-11 · Recommendation

Reminds me of the AI competition on infinite mario, recently: Here is a video of the winning bot (A* based approach), cool to watch, as they display its trajectory calculations. [VIDEO] GA is cool and everything, but I am sometimes sceptical about its merits. Yes, it gives you a natural problem representation, but would it really have done a better job in this particular domain than using a technique like simulated annealing to move through the Mario AI parameter space? We just don't know without seeing benchmark comparisons. Maybe its much worse. So, its a cool application, but we should be cautious about how quickly we decide GA was a particularly suitable search algorithm.

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Radix · 2009-08 · Recommendation

If you are impressed with this one follow the link in the video to here: [VIDEO] For all my hours as a kid I was never this good, I never even realized I could kill the pipe fish by jumping from below.

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Sources

Video by Robin Baumgarten, played through YouTube.

Data and selection notes

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