An Olympic machine learning endeavor
We will create a machine learning program that learns how to play QWOP, a browser-based video game in which the player needs to control an Olympic athlete by moving his thighs and calves with the Q, W, O, and P keys on the keyboard.
Machine learning is a new topic for all of us, which presents the challenge of the “new technology learning curve.” This project may especially benefit from critical path analysis and a Gantt chart, because significant research into machine learning will need to be done upfront to avoid laying a bad foundation for the rest of the app. However, our team will need to work concurrently on various parts of the game during this research period to avoid a bottleneck in development. As long as we can lay out a clear development strategy, we should be able to mitigate the effects of the new technology learning curve.
Benefits to the Customer
Videos of QWOP-AI playing the game has the potential for significant revenue on YouTube or other streaming media channels. Gameplay footage is one of the most popular types of online streaming video. The #1 most popular YouTube channel for several years has been PewDiePie, which features video gameplay footage with commentary and pulls in over $10 million per year.
We will deliver a Minimum Viable Product (MVP) with the following functionality:
- Learns how to play the QWOP video game.
- Improves its performance (fitness) over time as it plays and receives feedback.
- Provides persistent storage of learned information.
- Capable of playback at different stages of learning.
The MVP and the extended product will be delivered to the client by 6 December 2016.
Maintenance requests for QWOP-AI can be made by creating new GitHub Issues in the repository.
- 8 November 2016: Team Composition form submitted.
- 12 November 2016: Project Proposal submitted. Planning begins.
- 17 November 2016: Planning complete. Development begins.
- 1 December 2016: Minimum Viable Product completed. Retrospective Study begins.
- 6 December 2016: Product and Retrospective Study delivered. Submission time deadline: 8:00 pm
Time permitting, our developers will try different machine learning approaches in order to have the best chance of satisfactory results. These learning approaches include, but are not limited to:
- Decision tree learning
- Artificial neural networks
- Reinforcement learning
- Genetic algorithms
The original website for QWOP can be found at https://www.foddy.net/Athletics.html.
International Justice League of Super Acquaintances (IJLSA)
- Brody Concannon
- Nathan Karasch
- Stefan Kraus
- Gregory Steenhagen