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.

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Date Posted: 27 November 2017