مشخصات پژوهش

عنوان	A Least-Squares Temporal Difference based method for solving resource allocation problems
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Least-squares temporal difference, Approximate dynamic programming, Markov decision process, Birth–death process, Monte Carlo simulations
چکیده	Value function approximation has a central role in Approximate Dynamic Programming (ADP) to overcome the so-called curse of dimensionality associated to real stochastic processes. In this regard, we propose a novel Least-Squares Temporal Difference (LSTD) based method: the “Multi-trajectory Greedy LSTD” (MG-LSTD). It is an exploration-enhanced recursive LSTD algorithm with the policy improvement embedded within the LSTD iterations. It makes use of multi-trajectories Monte Carlo simulations in order to enhance the system state space exploration. This method is applied for solving resource allocation problems modeled via a constrained Stochastic Dynamic Programming (SDP) based framework. In particular, such problems are formulated as a set of parallel Birth–Death Processes (BDPs). Some operational scenarios are defined and solved to show the effectiveness of the proposed approach. Finally, we provide some experimental evidence on the MG-LSTD algorithm convergence properties in function of its key-parameters.
پژوهشگران	علی فروتنی (نفر اول)، ماسیمو تیپالدی (نفر دوم)، مجید غنی ئی زارچ (نفر سوم)، دیوید لیوزا (نفر چهارم)، لوئیجی گلیلمو (نفر پنجم)