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Reinforcement learning with knowledge by using a stochastic gradient method on a Bayesian network

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2 Author(s)
Yamamura, M. ; Tokyo Inst. of Technol., Japan ; Onozuka, T.

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