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.
Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-1479 |
Date | 03 July 2006 |
Creators | White, Spencer Kesson |
Publisher | BYU ScholarsArchive |
Source Sets | Brigham Young University |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Theses and Dissertations |
Rights | http://lib.byu.edu/about/copyright/ |
Page generated in 0.0075 seconds