Policy Iteration
Policy Iteration is a dynamic programming algorithm in the context of Markov Decision Processes that iteratively improves a policy by alternating between policy evaluation, which computes the value function for the current policy, and policy improvement, which updates the policy using the greedy action-selection rule with respect to the computed value function.
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Reinforcement Learning: An Introduction
Richard S. Sutton, Andy Barto · IEEE Transactions on Neural Networks · 1998 · 27.1K citations
Reinforcement Learning: An Introduction
IEEE Transactions on Neural Networks · 2005 · 25.8K citations
Reinforcement Learning: An Introduction
Jeffrey D. Johnson, Jinghong Li, Zengshi Chen · Neurocomputing · 2000 · 8.7K citations
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Chinese Academy of Sciences
Beijing, China
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Shandong Institute of Automation
Jinan, China
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90 publications
9.2K citations
Massachusetts Institute of Technology
Cambridge, United States
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97 publications
11.7K citations
Carnegie Mellon University
Pittsburgh, United States
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University of Massachusetts Amherst
Amherst Center, United States
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70 publications
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Northeastern University
Shenyang, China
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University of California, Berkeley
Berkeley, United States
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Stanford University
Stanford, United States
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