Efficient exploration through active learning for value function approximation in reinforcement learning-Reference-Cited by-同舟云学术

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Efficient exploration through active learning for value function approximation in reinforcement learning

Published:2010-06 Issue:5 Volume:23 Page:639-648
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Akiyama Takayuki,Hachiya Hirotaka,Sugiyama Masashi

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference22 articles.

1. Pattern recognition and machine learning;Bishop,2006

2. R-max—A general polynomial time algorithm for near-optimal reinforcement learning;Brafman;Journal of Machine Learning Research,2002

3. Active learning with statistical models;Cohn;Journal of Artificial Intelligence Research,1996

4. Theory of optimal experiments;Fedorov,1972

5. Adaptive importance sampling for value function approximation in off-policy reinforcement learning;Hachiya;Neural Networks,2009

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1. Exploration in deep reinforcement learning: A survey;Information Fusion;2022-09

2. Learning Inverse Statics Models Efficiently With Symmetry-Based Exploration;Frontiers in Neurorobotics;2018-10-23

3. Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation;Neural Networks;2014-09

4. Ruminative Reinforcement Learning: Improve Intelligent Inventory Control by Ruminating on the Past;Journal of Computers;2014-07-01

5. Intelligent Inventory Control via Ruminative Reinforcement Learning;Journal of Applied Mathematics;2014

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