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A cautionary comment: in the infinite horizon case, the discount factor has the function of guaranteeing the existence of a finite value. VII we briefly contact the analysis of the game for a finite horizon. In Sec. IV we derive the analysis of finest responses whereas symmetric Nash equilibria are characterized in Sec. The worth function and the coverage weights are up to date utilizing a concurrent studying-based mostly least squares method to reduce the instantaneous BEs and the BEs evaluated at preselected factors. It is shown that beneath a situation milder than PE, uniformly in the end bounded (UUB) convergence of the worth operate weights and the coverage weights to their true values could be established. POSTSUBSCRIPT. The next assumption, which normally is weaker than the PE assumption, is required for convergence of the concurrent learning-based worth function weight estimates. POSTSUBSCRIPT. The usage of two different sets of estimates to approximate the identical set of excellent weights is motivated by the following stability analysis and the truth that it facilitates an approximation of the BEs that is affine in the worth operate weights, enabling least squares-primarily based adaptation. In this paper our analysis is restricted to the viewcount. A linear cumulative value for acceleration can also be certainly one of our assumptions in the rest of the paper.

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