Statistical Reinforcement Learning: Modern Machine Learning Approaches (Hardcover)

Masashi Sugiyama

買這商品的人也買了...

商品描述

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 game players 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 RLm. The book provides a bridge between RL and data mining and machine learning research.

商品描述(中文翻譯)

強化學習(Reinforcement learning,簡稱RL)是一種基於大量數據在未知環境中進行決策的框架。近年來,已成功探索了幾個實際的RL應用,包括商業智能、工廠控制和遊戲玩家。本書提供了對這一領域的易於理解的介紹,涵蓋了基於模型和無模型的方法、策略迭代和策略搜索方法。書中提供了實例和最新的研究成果,包括在RL中的維度降低和風險敏感的RL。本書搭建了RL與數據挖掘和機器學習研究之間的橋樑。