Horizon-based Value Iteration

We present a horizon-based value iteration algorithm called Reverse Value Iteration (RVI). Empirical results on a variety of domains, both synthetic and real, show RVI often yields speedups of several orders of magnitude. RVI does this by ordering backups by horizons, with preference given to closer horizons, thereby avoiding many unnecessary and incorrect backups. We also compare to related work, including prioritized and partitioned value iteration approaches, and show that our technique performs favorably. The techniques presented in RVI are complementary and can be used in conjunction with previous techniques. We prove that RVI converges and often has better (but never worse) complexity than standard value iteration. To the authors’ knowledge, this is the first comprehensive theoretical and empirical treatment of such an approach to value iteration