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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Language English ● Format PDF ● Pages 206 ● ISBN 9781439856901 ● Publisher CRC Press ● Published 2015 ● Downloadable 3 times ● Currency EUR ● ID 4065783 ● Copy protection Adobe DRM
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