Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
J. Bagnell, Sham M. Kakade, Jeff Schneider, Andrew Ng
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.