Название: Statistical Reinforcement Learning: Modern Machine Learning Approaches Автор: Masashi Sugiyama
Издательство: Chapman and Hall/CRC
Серия: Chapman & Hall/Crc Machine Learning & Pattern Recognition
Год: 2015
Язык: английский
Формат: pdf (true)
Размер: 10.16 MB
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.
