Reinforcement Programming: A New Technique in Automatic Algorithm Development

Reinforcement programming is a new technique for using computers to automatically create algorithms. By using the principles of reinforcement learning and Q-learning, reinforcement programming learns programs based on example inputs and outputs. State representations and actions are provided. A transition function and rewards are defined. The system is trained until the system converges on a policy that can be directly implemented as a computer program. The efficiency of reinforcement programming is demonstrated by comparing a generalized in-place iterative sort learned through genetic programming to a sorting algorithm of the same type created using reinforcement programming. The sort learned by reinforcement programming is a novel algorithm. Reinforcement programming is more efficient and provides a more effective solution than genetic programming in the cases attempted. As additional examples, reinforcement programming is used to learn three binary addition problems.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• White, Spencer Kesson

• genetic reinforcement programming Q-learning RPSort algorithm automatic
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/480

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-1479