We reveal a link between particle filtering methods and direct policy search reinforcement learning, and propose a novel reinforcement learning algorithm, based heavily on ideas borrowed from particle filters. A major advantage of the proposed algorithm is its ability to perform global search in policy space and thus find the globally optimal policy. We validate the approach on one- and two-dimensional problems with multiple optima, and compare its performance to a global random sampling method, and a state-of-the-art ExpectationMaximization based reinforcement learning algorithm
