Efficient Linear Approximations to Stochastic Vehicular Collision-Avoidance Problems
نویسندگان
چکیده
The key components of an intelligent vehicular collision-avoidance system are sensing, evaluation, and decision making. We focus on the latter task of finding (approximately) optimal collision-avoidance control policies, a problem naturally modeled as a Markov decision process. However, standard MDP models scale exponentially with the number of state features, rendering them inept for large-scale domains. To address this, factored MDP representations and approximation methods have been proposed. We approximate collisionavoidance factored MDP using a composite approximate linear programming approach that symmetrically approximates objective functions and feasible regions of the LP. We show empirically that, combined with a novel basis-selection method, this produces high-quality approximations at very low computational cost.
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