Statistical Reinforcement Learning

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文件名称：Statistical Reinforcement Learning
文件大小：7.22MB
文件格式：PDF
更新时间：2022-01-05 14:19:20
深度學習 强化學習 統計學習 This book by Prof. Masashi Sugiyama covers the range of reinforcement learning algorithms from a fresh, modern perspective. With a focus on the statistical properties of estimating parameters for reinforcement learning, the book relates a number of different approaches across the gamut of learning scenarios. The algorithms are divided into model-free approaches that do not explicitly model the dynamics of the environment, and model-based approaches that construct descriptive process models for the environment. Within each of these categories, there are policy iteration algorithms which estimate value functions, and policy search algorithms which directly manipulate policy parameters