Return to Dr. Jacob Schrum's Homepage

NEAT and HyperNEAT Experiments in Tetris

This page presents research in Tetris done by undergraduate students Lauren Gillespie and Gabriela Gonzalez as part of Southwestern University's Summer research program SCOPE. Note that this research has since been extended and improved upon. The research makes use of the MM-NEAT software package (an extension of NEAT), which has been extended to include HyperNEAT. State evaluators are evolved to play the game of Tetris using both raw screen inputs (taken from the 10 by 20 grid) and hand-designed features (commonly used in previous Tetris research) using both HyperNEAT and standard NEAT. The videos on this page can be viewed in a playlist here.

NEAT Using Raw Screen Inputs

NEAT using raw screen inputs is unable to learn how to play the game. The agent uses a delaying tactic of moving pieces to the sides of the board before filling the center. This tactic results in a very small number of lines being cleared.

HyperNEAT Using Raw Screen Inputs

HyperNEAT using raw screen inputs plays Tetris much better than regular NEAT. The agent is able to clear many more lines than the NEAT agent, though it does lose before too long. Decent performance using raw inputs is impressive, but still cannot compete with using simple hand-designed features.

HyperNEAT Using Hand-Designed Features

HyperNEAT performs much better with hand-designed features than with raw screen inputs. In fact, the geometric awareness of HyperNEAT allows it to quickly shoot up in performance and do better than standard NEAT (with hand-designed features) in early generations. However, HyperNEAT's performance plateaus early and stays steady, even though there is still room for improvement.

NEAT Using Hand-Designed Features

This particular NEAT champion using hand-designed features performs much better than other HyperNEAT champions, though most NEAT champions perform worse. NEAT runs learn slower than HyperNEAT and tend to be worse, but as this video shows, the best NEAT results are better than the best HyperNEAT results when using hand-designed features.

Associated Publications


Peer-Reviewed Conference Publications


Undergraduate Poster Presentations Supervised


Associated Movies


Miscellaneous Content

  • Fall 2018: Evolutionary Computation Applied to Digital Entertainment and the Arts, poster presented at the President's Appreciation Celebration for Southwestern University donors.
  • Summer 2018: Neuroevolution in Video Games: "Mad Science Monday" presentation made by my SCOPE Summer research students to present to other SCOPE students
  • Spring 2018: Comparing Direct and Indirect Encodings Using Both Raw and Hand-Designed Features in Tetris, presentation by Lauren Gillespie at the Southwestern University Undergraduate Research & Creative Works Symposium
  • Summer 2016: Computer Science Majors Develop Artificial Intelligence for Video Games, an article about my Summer SCOPE research.
  • Summer 2016: Evolutionary Computation for Creativity and Intelligence: "Mad Science Monday" presentation made by my SCOPE Summer research students to present to other SCOPE students

  • Last Updated: 5/28/2019