Policy Search by Dynamic Programming
Conference Paper, Proceedings of (NeurIPS) Neural Information Processing Systems, pp. 831 - 838, December, 2003
Abstract
We consider the policy search approach to reinforcement learning. We show that if a ``baseline distribution'' is given (indicating roughly how often we expect a good policy to visit each state), then we can derive a policy search algorithm that terminates in a finite number of steps, and for which we can provide non-trivial performance guarantees. We also demonstrate this algorithm on several grid-world POMDPs, a planar biped walking robot, and a double-pole balancing problem.
BibTeX
@conference{Bagnell-2003-8823,author = {J. Andrew (Drew) Bagnell and Sham Kakade and Andrew Ng and Jeff Schneider},
title = {Policy Search by Dynamic Programming},
booktitle = {Proceedings of (NeurIPS) Neural Information Processing Systems},
year = {2003},
month = {December},
pages = {831 - 838},
publisher = {MIT Press},
keywords = {Reinforcement Learning, Control, Policy Search, POMDP, partial observability, dynamic programming},
}
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