Increasing the flexibility and speed of convergence of a Learning Agent

Miguel A. Soto Santibanez, Michael Mahmoud Marefat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A review of the basic methods used to model a Learning Agent, such as Instance-Based Learning, Artificial Neural Networks and Reinforcement Learning, suggests that they either lack flexibility (can only be used to solve a small number of problems) or they tend to converge very slowly to the optimal policy. This paper describes and illustrates a set of processes that address these two shortcomings. The resulting Learning Agent is able to "adapt fairly well" to a much larger set of environments and is capable of doing this in a reasonable amount of time. In order to address the lack of flexibility and slow convergence to the optimal policy, the new Learning Agent becomes a hybrid between a L. A. based on Instance-Based Learning and one based on Reinforcement Learning. To accelerate its convergence to its optimal policy, this new Learning Agent incorporates the use of a new concept we call Propagation of Good Findings. Furthermore, to make a better use of the Learning Agent's memory resources, and therefore increase its flexibility, we make use of another new concept we call Moving Prototypes.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Pages1748-1753
Number of pages6
Volume3
Publication statusPublished - 2001
Event2001 IEEE International Conference on Systems, Man and Cybernetics - Tucson, AZ, United States
Duration: Oct 7 2001Oct 10 2001

Other

Other2001 IEEE International Conference on Systems, Man and Cybernetics
CountryUnited States
CityTucson, AZ
Period10/7/0110/10/01

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ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

Cite this

Soto Santibanez, M. A., & Marefat, M. M. (2001). Increasing the flexibility and speed of convergence of a Learning Agent. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 3, pp. 1748-1753)