{"id":{"repo_id":"byu","oai_identifier":"oai:scholarsarchive.byu.edu:etd-1479"},"canonical_url":"https://search.dev.ndltd.org/etd/byu/oai:scholarsarchive.byu.edu:etd-1479","repository":{"repo_id":"byu","name":"Brigham Young University","base_url":"https://scholarsarchive.byu.edu/do/oai/"},"display":{"title":"Reinforcement Programming: A New Technique in Automatic Algorithm Development","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["White, Spencer Kesson"],"institution":"Brigham Young University - Provo","degree_name":"MS","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T01:27:55Z","subjects":["genetic reinforcement programming Q-learning RPSort algorithm automatic","Computer Sciences"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsarchive.byu.edu/etd/480","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["White, Spencer Kesson"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2006-07-03T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["Brigham Young University - Provo"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["genetic reinforcement programming Q-learning RPSort algorithm automatic","Computer Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsarchive.byu.edu/etd/480","https://scholarsarchive.byu.edu/context/etd/article/1479/viewcontent/ETD_CISOPTR_700.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Physical and Mathematical Sciences; Computer Science"]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["application:pdf"]},{"key":"dc:source","label":"Dc Source","values":["Brigham Young University - Provo"]},{"key":"dc:title","label":"Title","values":["Reinforcement Programming: A New Technique in Automatic Algorithm Development"]}]}],"canonical_facts":{"dc:creator":["White, Spencer Kesson"],"dc:date":["2006-07-03T07:00:00Z"],"dc:description":["Physical and Mathematical Sciences; Computer Science"],"dc:description.abstract":["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."],"dc:format":["application:pdf"],"dc:identifier":["https://scholarsarchive.byu.edu/etd/480","https://scholarsarchive.byu.edu/context/etd/article/1479/viewcontent/ETD_CISOPTR_700.pdf"],"dc:language":["English"],"dc:publisher":["Brigham Young University - Provo"],"dc:source":["Brigham Young University - Provo"],"dc:subject":["genetic reinforcement programming Q-learning RPSort algorithm automatic","Computer Sciences"],"dc:title":["Reinforcement Programming: A New Technique in Automatic Algorithm Development"],"dc:type":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T01:27:55Z"}