{
"item_title" : "Statistical Reinforcement Learning",
"item_author" : [" Masashi Sugiyama "],
"item_description" : "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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Statistical Reinforcement Learning : Modern Machine Learning Approaches
Other Available Formats
Overview
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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Details
- ISBN-13: 9781439856895
- ISBN-10: 1439856893
- Publisher: CRC Press
- Publish Date: April 2015
- Dimensions: 9.3 x 6.1 x 0.7 inches
- Shipping Weight: 0.92 pounds
- Page Count: 206
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