Global Linear Convergence of Online Reinforcement Learning for Partially Observable Systems
Author:
Affiliation:
1. Graduate School of Informatics and Engineering, The University of Electro-Communications,Department of Mechanical and Intelligent Systems Engineering,Chofu, Tokyo,Japan,182-8585
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837955/9837961/09838197.pdf?arnumber=9838197
Reference20 articles.
1. Global exponential convergence of gradient methods over the nonconvex landscape of the linear quadratic regulator
2. Random search for learning the linear quadratic regulator
3. Policy gradient methods for reinforcement learning with function approximation;sutton;Advances in neural information processing systems,0
4. Mastering the game of Go with deep neural networks and tree search
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