Statistical Reinforcement Learning Modern Machine Learning Approaches
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.
Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods.
- Covers the range of reinforcement learning algorithms from a modern perspective
- Lays out the associated optimization problems for each reinforcement learning scenario covered
- Provides thought-provoking statistical treatment of reinforcement learning algorithms
The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques.
This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.
Introduction to Reinforcement Learning. Model-Free Policy Iteration. Policy Iteration with Value Function Approximation. Basis Design for Value Function Approximation. Sample Reuse in Policy Iteration. Active Learning in Policy Iteration. Robust Policy Iteration. Model-Free Policy Search. Direct Policy Search by Gradient Ascent. Direct Policy Search by Expectation-Maximization. Policy-Prior Search. Model-Based Reinforcement Learning. Transition Model Estimation. Dimensionality Reduction for Transition Model Estimation.
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.... It is a contemporary and welcome addition to the rapidly growing machine learning literature. Both beginner students and experienced researchers will find it to be an important source for understanding the latest reinforcement learning techniques.
—Daniel D. Lee, GRASP Laboratory, School of Engineering and Applied Science, University of Pennsylvania