Super mario evolution

117 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Breeding a diversity of Super Mario behaviors through interactive evolution (2016, unknown): “Projects range from imitating player behavior [10], evolving behavior trees through grammatical evolution [12], or creating content based on player experience [11], to Mario controllers optimized through reinforcement learning [20].”
A Neuroevolution Approach to General Atari Game Playing (2014, background): “Research in this area has focused on learning to play single games using perceptual imagery [52], human demonstration [9], discovery of objective functions [48], and use of raw visual inputs [30], [34], [35], [49].”
Playing SNES in the Retro Learning Environment (2016, methodology): “During the competition, several algorithms were trained on Infinite Mario and their performances were measured in terms of the number of stages completed.”
The 2009 Mario AI Competition (2010, methodology): “described experiments in evolving neural network controllers for the same game as was used in the competition, using an earlier version of the API [3].”