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Researchers develop AI that can play Angry Birds

[2019.10.08, Tue 16:05] It wasn't until recently that Angry Birds inspired AI designed to beat the game's top players - or at the very least, achieve performance at par. In a preprint paper published this week on Arxiv.org, researchers at Charles University in Prague detail an AI system - - DQ-Birds - trained using Deep Q-learning, a technique pioneered by Alphabet's DeepMind that instructs an agent which action to take under what circumstances using a random sample of prior actions. "Angry Birds appears to be a difficult task to solve for artificially intelligent agents due to the sequential decision-making, non-deterministic game environment, enormous state and action spaces and requirement to differentiate between multiple birds, their abilities and optimum tapping times," wrote the researchers. To learn their model, the team compiled a data set of 21 levels of Angry Birds Classic's Poached Eggs level consisting of over 115,000 screenshots. The researchers report that their agent was able to surpass a group of four expert humans players' scores on some levels, but that it fell short in terms of the total sum of obtained scores from 21 levels. Separately, the team submitted their model for consideration in the Angry Birds AI Competition, an annual competition held during the IJCAI conference that has agents solve eight previously unseen Angry Birds levels in three rounds. "One of the goals that we did not quite achieve in this work, is to outperform humans in Angry Birds," wrote the coauthors, who chalk the system's shortcomings up to a training data set that lacked sufficient level diversity.
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