Robust Value Function Approximation Using Bilinear Programming

Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)

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Marek Petrik, Shlomo Zilberstein


Existing value function approximation methods have been successfully used in many applications, but they often lack useful a priori error bounds. We propose approximate bilinear programming, a new formulation of value function approximation that provides strong a priori guarantees. In particular, it provably finds an approximate value function that minimizes the Bellman residual. Solving a bilinear program optimally is NP hard, but this is unavoidable because the Bellman-residual minimization itself is NP hard. We, therefore, employ and analyze a common approximate algorithm for bilinear programs. The analysis shows that this algorithm offers a convergent generalization of approximate policy iteration. Finally, we demonstrate that the proposed approach can consistently minimize the Bellman residual on a simple benchmark problem.