Referenced Items are not available for this document.
For real applications of reinforcement learning, it is necessary to reduce the number of trial-and-errors. The paper proposes a method to use knowledge in reinforcement learning. We have regarded a Bayesian network as a stochastic policy, and adapted a rigid propagation procedure for a stochastic gradient method. We made preliminary experiments to demonstrate our method in a robot navigation task
Published in:
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
(Volume:3
)
Date of Conference: 4-9 May 1998
- Page(s):
- 2045 - 2050 vol.3
- Meeting Date :
- 04 May 1998-09 May 1998
- ISSN :
- 1098-7576
- Print ISBN:
- 0-7803-4859-1
- INSPEC Accession Number:
- 6047101
- Conference Location :
- Anchorage, AK
- Digital Object Identifier :
- 10.1109/IJCNN.1998.687174
- Product Type:
- Conference Publications
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