Tags
Language
Tags
July 2025
Su Mo Tu We Th Fr Sa
29 30 1 2 3 4 5
6 7 8 9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30 31 1 2
    Attention❗ To save your time, in order to download anything on this site, you must be registered 👉 HERE. If you do not have a registration yet, it is better to do it right away. ✌

    ( • )( • ) ( ͡⚆ ͜ʖ ͡⚆ ) (‿ˠ‿)
    SpicyMags.xyz

    Statistical Reinforcement Learning: Modern Machine Learning Approaches

    Posted By: AlenMiler
    Statistical Reinforcement Learning: Modern Machine Learning Approaches

    Statistical Reinforcement Learning: Modern Machine Learning Approaches (Chapman & Hall/Crc Machine Learning & Pattern Recognition) by Masashi Sugiyama
    English | Mar. 16, 2015 | ISBN: 1439856893 | 206 Pages | PDF | 7.88 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.

    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.