Bayesian Reinforcement Learning: A Survey

著者

    • Ghavamzadeh, Mohammad
    • Mannor, Shie
    • Pineau, Joelle
    • Tamar, Aviv

書誌事項

Bayesian Reinforcement Learning: A Survey

Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar

(Foundations and trends in machine learning, v. 8, issue 5-6)

now Publishers, c2015

大学図書館所蔵 件 / 1

この図書・雑誌をさがす

注記

Includes bibliographical references (p. 123-135)

内容説明・目次

内容説明

Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. It is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.

目次

1: Introduction 2: Technical Background 3: Bayesian Bandits 4: Model-based Bayesian Reinforcement Learning 5: Model-free Bayesian Reinforcement Learning 6: Risk-aware Bayesian Reinforcement Learning 7: BRL Extensions 8: Outlook Acknowledgements Appendices References

「Nielsen BookData」 より

関連文献: 1件中  1-1を表示

詳細情報

  • NII書誌ID(NCID)
    BB21312680
  • ISBN
    • 9781680830880
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Boston
  • ページ数/冊数
    x, 135 p.
  • 大きさ
    24 cm
  • 親書誌ID
ページトップへ