Titlebook: Intergenerational Income Mobility and Redistributive Policy; Mareike Schad Book 2016 Springer Fachmedien Wiesbaden 2016 Distribution Polic

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Mareike Schad. Traditionally, system integration has been facilitated by forcing a common interface layer on each of the systems involved. However, autonomous administration causes asynchrony of service evolution and variation in common service behaviour in each system. In such an environment, a single middlewar

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Mareike Schad. Traditionally, system integration has been facilitated by forcing a common interface layer on each of the systems involved. However, autonomous administration causes asynchrony of service evolution and variation in common service behaviour in each system. In such an environment, a single middlewar

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Mareike Schadg due to its combinatorial nature. First, with an exponentially scaling action set, it is challenging to search effectively and find the right balance between exploration and exploitation. Second, performing maximization over all agents’ actions jointly is computationally intractable. To tackle thes

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Mareike Schadagent control, systems are complex with unknown or highly uncertain dynamics, where traditional model-based control methods can hardly be applied. Compared with model-based control in control theory, deep reinforcement learning (DRL) is promising to learn the controller/policy from data without the

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Mareike Schadhave emerged in the industry. DECOPs refer to optimization problems with objective and constraint functions that are computationally expensive and can only be evaluated on multiple agents of distributed networks. In DECOPs, the raw data of each agent cannot be transmitted to other agents, but only o