Statistics Papers
Document Type
Conference Paper
Date of this Version
2003
Publication Source
Advances in Neural Information Processing Systems
Volume
16
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.
Recommended Citation
Bagnell, J. A., Kakade, S., Ng, A. Y., & Schneider, J. G. (2003). Policy Search by Dynamic Programming. Advances in Neural Information Processing Systems, 16 Retrieved from https://repository.upenn.edu/statistics_papers/465
Date Posted: 27 November 2017