Artificial Intelligence – Robert Wright, Lei Yu, Steven Loscalzo, Research Foundation of State University of New York
Abstract for “Approximate Value Iteration with Complex Returns by Bounding”
A system and method of controlling a system. This includes estimating the optimal control strategy for the system. Receiving data representing sequential states with associated trajectories of that system. Using complex returns of the received data to estimate a bound dependent upon at least the off policy trajectories to improve the estimation of its value. The output signal can be used to direct the system or the optimal control policy. It is best to use a reinforcement learning system that continuously improves.
Background for “Approximate Value Iteration with Complex Returns by Bounding”
“List of Abbreviations.”
“Example 1?”Complex Return Fitted QIteration
“Example 2?” Fitted Q-Iteration
“Example 3” (Comparative Example). Trajectory Fitted QIteration.
“Example 4?” Inverse Return Function FQI
“Example 5?Empirical Results”
Summary for “Approximate Value Iteration with Complex Returns by Bounding”
“List of Abbreviations.”
“Example 1?”Complex Return Fitted QIteration
“Example 2?” Fitted Q-Iteration
“Example 3” (Comparative Example). Trajectory Fitted QIteration.
“Example 4?” Inverse Return Function FQI
“Example 5?Empirical Results”
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