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k-Certainty Exploration Method: an action selector to identify the environment in reinforcement learning

  • Published:1997-03 Issue:1 Volume:91 Page:155-171
  • ISSN:0004-3702
  • Container-title:Artificial Intelligence
  • language:en
  • Short-container-title:Artificial Intelligence

Author:

Miyazaki Kazuteru,Yamamura Masayuki,Kobayashi Shigenobu

Publisher

Elsevier BV

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference23 articles.

1. Dynamic programming and stochastic control;Bertsekas,1976

2. Reinforcement learning applied to linear quadratic regulation;Bradtke,1993

3. A teaching method for reinforcement learning;Clouse,1992

4. Credit assignment in rule discovery systems based on genetic algorithms;Grefenstette;Mach. Learn.,1988

5. Cognitive systems based on adaptive algorithms;Holland,1987

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1. Formalizing Model-Based Multi-Objective Reinforcement Learning With a Reward Occurrence Probability Vector;Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning;2022

2. Distributed Deep Reinforcement Learning Method Using Profit Sharing for Learning Acceleration;IEEJ Transactions on Electrical and Electronic Engineering;2020-06-27

3. Developing adaptive traffic signal control by actor–critic and direct exploration methods;Proceedings of the Institution of Civil Engineers - Transport;2019-10

4. Proposal and Evaluation of an Indirect Reward Assignment Method for Reinforcement Learning by Profit Sharing Method;Advances in Intelligent Systems and Computing;2018-11-09

5. Exploitation-Oriented Learning with Deep Learning – Introducing Profit Sharing to a Deep Q-Network –;Journal of Advanced Computational Intelligence and Intelligent Informatics;2017-09-20
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