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.
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