Noisy Exploration
Noisy Exploration is a stochastic exploration strategy in reinforcement learning where randomness is explicitly injected into the action selection process to facilitate exploration, distinguishing it from deterministic or purely optimistic exploration methods by relying on probabilistic perturbations of policies or value estimates.
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2017–2026
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STOMP: Stochastic trajectory optimization for motion planning
Mrinal Kalakrishnan, Sachin Chitta, Evangelos A. Theodorou et al. · 2011 · 881 citations
Parameter Space Noise for Exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal et al. · arXiv (Cornell University) · 2017 · 368 citations · Full text
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University of Southern California
Los Angeles, United States
2 h-index
9 publications
3.7K citations
Max Planck Institute for Human Development
Berlin, Germany
2 h-index
6 publications
609 citations

University of Erfurt
Erfurt, Germany
2 h-index
2 publications
203 citations
King's College London
London, United Kingdom
1 h-index
1 publications
15 citations
Columbia University
New York, United States
1 h-index
1 publications
16 citations
University of Memphis
Memphis, United States
1 h-index
3 publications
33 citations

Harbin Institute of Technology
Harbin, China
1 h-index
2 publications
26 citations

European Space Research and Technology Centre
Noordwijk-Binnen, The Netherlands
1 h-index
1 publications
15 citations
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Repository · 4 publications · 456 citations
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JMLR · 1 publications · 56 citations
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Repository · 1 publications · 35 citations
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1 publications · 12 citations
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1 publications · 274 citations
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