Policy Search by Dynamic Programming
James Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff Schneider · ScholarlyCommons (University of Pennsylvania) · 2018
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.