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Empirical Inference Article 2010

Policy gradient methods

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Empirical Inference
Jan Peters
Research Group Leader

Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. They do not suffer from many of the problems that have been marring traditional reinforcement learning approaches such as the lack of guarantees of a value function, the intractability problem resulting from uncertain state information and the complexity arising from continuous states & actions.

Author(s): Peters, J.
Journal: Scholarpedia
Volume: 5
Number (issue): 11
Pages: 3698
Year: 2010
Month: November
Day: 0
Bibtex Type: Article (article)
DOI: 10.4249/scholarpedia.3698
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@article{6940,
  title = {Policy gradient methods},
  journal = {Scholarpedia},
  abstract = {Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. They do not suffer from many of the problems that have been marring traditional reinforcement learning approaches such as the lack of guarantees of a value function, the intractability problem resulting from uncertain state information and the complexity arising from continuous states & actions.},
  volume = {5},
  number = {11},
  pages = {3698},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2010},
  slug = {6940},
  author = {Peters, J.},
  month_numeric = {11}
}