Clustering experience replay for the effective exploitation in reinforcement learning

ISBFK-means: A new clustering algorithm based on influence space

(2022) Yuqing Yang et al.   EXPERT SYSTEMS WITH APPLICATIONS

Adaptive exploration policy for exploration–exploitation tradeoff in continuous action control optimization

(2021) Min Li et al.   International Journal of Machine Learning and Cybernetics

Weighted clustering ensemble: A review

(2021) Mimi Zhang   PATTERN RECOGNITION

An adaptive kernelized rank-order distance for clustering non-spherical data with high noise

(2020) Tianyi Huang et al.   International Journal of Machine Learning and Cybernetics

Local gap density for clustering high-dimensional data with varying densities

(2019) Ruijia Li et al.   KNOWLEDGE-BASED SYSTEMS

How much can k-means be improved by using better initialization and repeats?

(2019) Pasi Fränti et al.   PATTERN RECOGNITION

Deep Clustering: On the Link Between Discriminative Models and K-Means

(2019) Mohammed Jabi et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

A review of clustering techniques and developments

(2017) Amit Saxena et al.   NEUROCOMPUTING

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Reinforcement learning in robotics: A survey

(2013) Jens Kober et al.   INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients

(2012) Ivo Grondman et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE

Experience Replay for Real-Time Reinforcement Learning Control

(2011) Sander Adam et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE