Optimistic Policy Iteration
Optimistic Policy Iteration is a variant of policy iteration in reinforcement learning where the policy evaluation step is truncated after a single iteration, allowing the algorithm to proceed to policy improvement based on an incomplete but optimistic estimate of the value function, thereby accelerating convergence in certain settings.
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29
Institutions
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2017–2026
10
Variants
Modified Policy Iteration
Parents
20
Provably Efficient Exploration in Policy Optimization
Qi Cai, Zhuoran Yang, Chi Jin et al. · 2020 · 86 citations
Model-free reinforcement learning as mixture learning
Nikos Vlassis, Marc Toussaint · 2009 · 42 citations · Full text
Convergence of Optimistic and Incremental Q-Learning
Eyal Even-Dar, Yishay Mansour · 2001 · 41 citations
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Princeton University
Princeton, United States
2 h-index
4 publications
206 citations
Johannes Gutenberg University Mainz
Mainz, Germany
2 h-index
2 publications
49 citations
New York University
New York, United States
2 h-index
4 publications
168 citations

Tel Aviv University
Tel Aviv, Israel
1 h-index
2 publications
82 citations
Shandong Institute of Automation
Jinan, China
1 h-index
3 publications
210 citations

Centre National de la Recherche Scientifique
Paris, France
1 h-index
1 publications
18 citations
University of Wisconsin–Madison
Madison, United States
1 h-index
2 publications
52 citations
University of Oxford
Oxford, United Kingdom
1 h-index
1 publications
10 citations
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Repository · 4 publications · 68 citations
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Repository · 1 publications · 11 citations
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Conference · 1 publications · 36 citations
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1 publications · 48 citations
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1 publications · 18 citations
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