A pulse neural network learning algorithm for POMDP environment

Koichiro Takita, Masafumi Hagiwara

Research output: Contribution to conferencePaper

Abstract

In this paper, we proposed a new pulse neural network model and its reinforcement learning algorithm. The main purpose of this model is to utilize pulse neuron's ability to handle sequential inputs in partially observable Markov decision process (POMDP). Its performance is confirmed by computer simulation.

Original languageEnglish
Pages1643-1648
Number of pages6
Publication statusPublished - 2002 Jan 1
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: 2002 May 122002 May 17

Other

Other2002 International Joint Conference on Neural Networks (IJCNN '02)
CountryUnited States
CityHonolulu, HI
Period02/5/1202/5/17

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Keywords

  • Pulse neural network
  • Reinforcement learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Takita, K., & Hagiwara, M. (2002). A pulse neural network learning algorithm for POMDP environment. 1643-1648. Paper presented at 2002 International Joint Conference on Neural Networks (IJCNN '02), Honolulu, HI, United States.