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Statistical Reinforcement Learning : Modern Machine Learning Approaches - Masashi Sugiyama

Statistical Reinforcement Learning

Modern Machine Learning Approaches

By: Masashi Sugiyama

eText | 16 March 2015 | Edition Number 1

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Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and gaming have been successfully explored in recent years. Providing an accessible introduction to the field, this book covers model-based and model-free approaches, policy iteration, and policy search methods. It presents illustrative examples and state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. The book provides a bridge between RL and data mining and machine learning research.

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Non-FictionEconomicsEconometricsEconomic StatisticsComputing & I.T.Computer ScienceArtificial IntelligenceDigital Lifestyle & Online World: Consumer & User GuidesComputer Games & Online Games Strategy GuidesDatabasesData MiningEngineering & Technology
Environmental ScienceElectronics & Communications EngineeringElectronics EngineeringAutomatic Control EngineeringMathematicsProbability & Statistics

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Published: 30th June 2020

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