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In this paper we introduce a control policy which we refer to as the iterated approximate value function policy. The generation of this policy requires two stages, the first one carried out off-line, and the second stage carried out on-line. In the first stage we simultaneously compute a trajectory of moments of the state and action and a sequence of approximate value functions optimized to that trajectory...
In this paper we describe an approximate dynamic programming policy for a discrete-time dynamical system perturbed by noise. The approximate value function is the pointwise supremum of a family of lower bounds on the value function of the stochastic control problem; evaluating the control policy involves the solution of a min-max or saddle-point problem. For a quadratically constrained linear quadratic...
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