Interpretable policies for reinforcement learning by empirical fuzzy sets-Reference-Cited by-同舟云学术

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Interpretable policies for reinforcement learning by empirical fuzzy sets

Published:2020-05 Issue: Volume:91 Page:103559
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Huang Jianfeng,Angelov Plamen P.,Yin Chengliang

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference43 articles.

1. Autonomous Learning Systems;Angelov,2012

2. Autonomous learning multi-model classifier of 0-order (ALMMo-0);Angelov,2017

3. Empirical fuzzy sets;Angelov;Int. J. Intell. Syst.,2017

4. Empirical Approach to Machine Learning;Angelov,2019

5. Empirical data analytics;Angelov;Int. J. Intell. Syst.,2017

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4. Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation;Energies;2023-04-28

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