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Policy Gradients with Parameter-based Exploration for Control


Conference Paper


We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in parameter space, which leads to lower variance gradient estimates than those obtained by policy gradient methods such as REINFORCE. For several complex control tasks, including robust standing with a humanoid robot, we show that our method outperforms well-known algorithms from the fields of policy gradients, finite difference methods and population based heuristics. We also provide a detailed analysis of the differences between our method and the other algorithms.

Author(s): Sehnke, F. and Osendorfer, C. and Rückstiess, T. and Graves, A. and Peters, J. and Schmidhuber, J.
Book Title: ICANN 2008
Journal: Artificial Neural Networks: ICANN 2008
Pages: 387-396
Year: 2008
Month: September
Day: 0
Editors: Kurkova-Pohlova, V. , R. Neruda, J. Koutnik
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-540-87536-9_40
Event Name: 18th International Conference on Artificial Neural Networks
Event Place: Praha, Czech Republic

Address: Berlin, Germany
Digital: 0
Institution: European Neural Network Society
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Policy Gradients with Parameter-based Exploration for Control},
  author = {Sehnke, F. and Osendorfer, C. and R{\"u}ckstiess, T. and Graves, A. and Peters, J. and Schmidhuber, J.},
  journal = {Artificial Neural Networks: ICANN 2008},
  booktitle = {ICANN 2008},
  pages = {387-396},
  editors = {Kurkova-Pohlova, V. , R. Neruda, J. Koutnik},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  institution = {European Neural Network Society},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2008},
  month_numeric = {9}