Knowledge-based recurrent neural networks in reinforcement learning

Tien Dung Le, Takashi Komeda, Motoki Takagi

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the 11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007
ページ169-174
ページ数6
出版ステータスPublished - 2007
イベント11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007 - Palma de Mallorca, Spain
継続期間: 2007 8月 292007 8月 31

出版物シリーズ

名前Proceedings 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
国/地域Spain
CityPalma de Mallorca
Period07/8/2907/8/31

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • ソフトウェア

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