315 research outputs found

    On the reduction of dimensionality for classes of dynamic programming processes

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    Time dependent scattering processes and invariant imbedding

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    Upper and lower bounds for the solutions of the matrix Riccati equation

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    Dynamic programming and the quadratic form of Selberg

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    Simplified analysis of a hyperbolic system

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    The method of generating equation is used in order to reduce a weakly nonlinear hyperbolic system to the standard form, i.e. the form which admits an asymptotic treatment based on the averaging principle.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25879/1/0000442.pd

    Online Learning Adaptation Strategy for DASH Clients

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    In this work, we propose an online adaptation logic for Dynamic Adaptive Streaming over HTTP (DASH) clients, where each client selects the representation that maximize the long term expected reward. The latter is defined as a combination of the decoded quality, the quality fluctuations and the rebuffering events experienced by the user during the playback. To solve this problem, we cast a Markov Decision Process (MDP) optimization for the selection of the optimal representations. System dynamics required in the MDP model are a priori unknown and are therefore learned through a Reinforcement Learning (RL) technique. The developed learning process exploits a parallel learning technique that improves the learning rate and limits sub-optimal choices, leading to a fast and yet accurate learning process that quickly converges to high and stable rewards. Therefore, the efficiency of our controller is not sacrificed for fast convergence. Simulation results show that our algorithm achieves a higher QoE than existing RL algorithms in the literature as well as heuristic solutions, as it is able to increase average QoE and reduce quality fluctuations
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