A fast point-based algorithm for POMDPs

We describe a point-based approximate value iteration algorithm for partially observable Markov decision processes. The algorithm performs value function updates ensuring that in each iteration the new value function is an upper bound to the previous value function, as estimated on a sampled set of belief points. A randomized belief-point selection scheme allows for fast update steps. Results indicate that the proposed algorithm achieves competitive performance, both in terms of solution quality