Knowledge-based recurrent neural networks in reinforcement learning

Tien Dung Le, Takashi Komeda, Motoki Takagi

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

2 Citations (Scopus)

Abstract

Recurrent Neural Networks (RNNs) have been shown to have a strong ability to solve some hard problems. Learning time for these problems from scratch is typically very long. For supervised learning, several methods have been proposed to reuse existing knowledge in previous similar tasks. However, for unsupervised learning such as Reinforcement Learning (RL), especially for Partially Observable Markov Decision Processes (POMDPs), it is difficult to apply directly these algorithms. This paper presents several methods which have the potential of transferring of knowledge in RL using RNN: Directed Transfer, Cascade-Correlation, Mixture of Expert Systems, and Two-Level Architecture. Preliminary results of experiments in the E maze domain show the potential of these methods. Knowledge based learning time for a new problem is much shorter learning time from scratch even thought the new task looks very different from the previous tasks.

Original languageEnglish
Title of host publicationProceedings of the 11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007
Pages169-174
Number of pages6
Publication statusPublished - 2007 Dec 1
Event11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007 - Palma de Mallorca, Spain
Duration: 2007 Aug 292007 Aug 31

Publication series

NameProceedings of the 11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007

Conference

Conference11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007
CountrySpain
CityPalma de Mallorca
Period07/8/2907/8/31

Keywords

  • Machine learning
  • Neural networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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  • Cite this

    Le, T. D., Komeda, T., & Takagi, M. (2007). Knowledge-based recurrent neural networks in reinforcement learning. In Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007 (pp. 169-174). (Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007).