Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

Zela Moraya, Wester Edison (2013). Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players. Tesis (Doctoral), Facultad de Informática (UPM) [antigua denominación].

Descripción

Título: Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players
Autor/es:
  • Zela Moraya, Wester Edison
Director/es:
  • Martinez Rey, Maria Aurora
  • Zato Recellado, Jose Gabriel
Tipo de Documento: Tesis (Doctoral)
Fecha: 7 Junio 2013
Materias:
Palabras Clave Informales: neuro-evolution, co-evolutionary algorithms, neural networks, games, artificial intelligence
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) (Tesis Doctoral) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (5MB) | Vista Previa

Resumen

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

Más información

ID de Registro: 15771
Identificador DC: http://oa.upm.es/15771/
Identificador OAI: oai:oa.upm.es:15771
Depositado por: Dr. Wester Edison Zela Moraya
Depositado el: 17 Jun 2013 06:29
Ultima Modificación: 21 Abr 2016 16:04
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM