Dynamic Policy Programming with Function Approximation
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:119-127, 2011.
In this paper, we consider the problem of planning in the infinite-horizon discounted-reward Markov decision problems. We propose a novel iterative method, called dynamic policy programming (DPP), which updates the parametrized policy by a Bellman-like iteration. For discrete state-action case, we establish sup-norm loss bounds for the performance of the policy induced by DPP and prove that it asymptotically converges to the optimal policy. Then, we generalize our approach to large-scale (continuous) state-action problems using function approximation technique. We provide sup-norm performance-loss bounds for approximate DPP and compare these bounds with the standard results from approximate dynamic programming (ADP) showing that approximate DPP results in a tighter asymptotic bound than standard ADP methods. We also numerically compare the performance of DPP to other ADP and RL methods. We observe that approximate DPP asymptotically outperforms other methods on the mountain-car problem.