Titlebook: Innovationen verbreiten, optimieren und evaluieren; Ein Leitfaden zur in Norbert Donner-Banzhoff,Stefan Bösner Book 2013 Springer-Verlag Be

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978-3-642-32039-2Springer-Verlag Berlin Heidelberg 2013

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irst start with the basic definitions and concepts of reinforcement learning, including the agent, environment, action, and state, as well as the reward function. Then, we describe a classical reinforcement learning problem, the bandit problem, to provide the readers with a basic understanding of th

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Norbert Donner-Banzhoff,Stefan Bösnerex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. .Divided into three main parts, this book provides a comp

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Norbert Donner-Banzhoff,Stefan Bösnerirst start with the basic definitions and concepts of reinforcement learning, including the agent, environment, action, and state, as well as the reward function. Then, we describe a classical reinforcement learning problem, the bandit problem, to provide the readers with a basic understanding of th

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Norbert Donner-Banzhoff,Stefan Bösnerg, to state-of-the-art multi-agent and meta learning..RevealDeep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, compute

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Norbert Donner-Banzhoff,Stefan Bösnerve significant impacts on mobile social networks (MSNs). MSNs can leverage these new paradigms to provide a new mechanism for users to share resources (e.g., information, computation-based services). In this chapter, we exploit the intrinsic nature of social networks, i.e., the trust formed through