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We present a simulation-based algorithm called Approximate Stochastic Annealing (ASA) for solving finite-horizon Markov decision processes (MDPs). The algorithm iteratively estimates the optimal policy by sampling from a sequence of probability distribution functions over the policy space. By exploiting a novel connection of ASA to the stochastic approximation method, we show that the sequence of...
We have identified a general class of nonlinear stochastic optimal control problems which can be reduced to computing the principal eigenfunction of a linear operator. Here we develop function approximation methods exploiting this inherent linearity. First we discretize the time axis in a novel way, yielding an integral operator that approximates not only our control problems but also more general...
We develop an iterative local dynamic programming method (iLDP) applicable to stochastic optimal control problems in continuous high-dimensional state and action spaces. Such problems are common in the control of biological movement, but cannot be handled by existing methods. iLDP can be considered a generalization of differential dynamic programming, in as much as: (a) we use general basis functions...
Previous studies have suggested that optimal control is one suitable model for biological movement. In some cases, solutions to optimal control problems are known, such as the Linear Quadratic Gaussian setting. However, more general cost functionals and nonlinear stochastic systems lead to optimal control problems to which direct solutions are presently unknown but these solutions would theoretically...
We consider control systems consisting of teams of agents operating in stochastic environments and communicating through a network with dynamic topology. An optimal centralized control policy can be derived from the Q-function associated with the problem. However, computing and storing the Q-function is intractable for systems of practical scale, and having a centralized policy may lead to prohibitive...
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