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The controller's optimal actions are obtained from an explicit solution of the optimization criteria, which enables fast real-time applications.
For example, the "administrative man" (such as the bureaucratic official depicted by Weber) goes about solving problems by relying on a highly routinized search for satisfactory (but not necessarily optimal) actions.
This vector computes optimal actions of all players considering their payoff function.
They try to maximize reward in the long run by identifying optimal actions.
Reinforcement learning makes use of the economic concept of utility to assign "points" for performing what are deemed to be optimal actions.
Every five minutes, the system polls thousands of sensors inside the data center and chooses the optimal actions based on this information.
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The arrow indicates the optimal action.
Once the optimal policy is obtained, the optimal action can be determined given the current state.
The optimal action of a node is determined by the following formulation: (1).
(a_{i}^=theta _{i}^(mathbf {x})) is the optimal action to queue i determined by θ ∗.
Optimal action is determined by using the partial information and the history of observations.
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