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• W2058455325 abstract "We introduce exploration in the TD-learning algorithm to approximate the value function for a given policy. In this way we can modify the norm used for approximation, “zooming in” to a region of interest in the state space. We also provide extensions to SARSA to eliminate the need for numerical integration in policy improvement. Construction of the algorithm and its analysis build on recent general results concerning the spectral theory of Markov chains and positive operators." @default.
• W2058455325 created "2016-06-24" @default.
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• W2058455325 date "2011-12-01" @default.
• W2058455325 modified "2023-10-16" @default.
• W2058455325 title "TD-learning with exploration" @default.
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• W2058455325 doi "https://doi.org/10.1109/cdc.2011.6160851" @default.
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