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Trey Smith and Reid G. Simmons. In Proc. Int. Conf. on Uncertainty in Artificial Intelligence (UAI), 2004.
We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known techniques: attention-focusing search heuristics and piecewise linear convex representations of the value function. HSVI's soundness and convergence have been proven. On some benchmark problems from the literature, HSVI, displays speedups of greater than 100 with respect to other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to a new rover exploration problem 10 times larger than most POMDP problems in the literature.
@InProceedings{smith04:hsvi,
author = {Trey Smith and Reid G. Simmons},
title = {Heuristic Search Value Iteration for {POMDPs}},
year = 2004,
booktitle = {Proc. Int. Conf. on Uncertainty in Artificial Intelligence (UAI)},
}
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