This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in stochastic adaptive control and reinforcement learning literature using model-based and modelfree approaches respectively. Both methods rely on solving a dynamic programming problem, either directly or indirectly, for finding the optimal closed loop control policy. The inherent 'curse of dimensionality' associated with dynamic programming method makes these approaches also computationally difficult.This paper proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, 'open loop -closed loop', approach. First, an openloop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, a closed loop control is developed around this open loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests significant reduction in training time compared to other state of the art algorithms.
This article makes a systematical description to the process of empty containers allocation, explains the subjective and objective reason which causes the empty container allocation, the characteristic of empty container allocation and the question which exists in the practice and the actual operation of container transportation, analyzes the goal and the major effect factors of empty containers allocation. Then considering the kinds of factors which affect the allocation of empty containers, the article establishes a liner programming model which not only accord with the characteristics of empty container allocation ,but also very easy to apply to shipping practice, and carry on a calculation and analysis to the liner programming model. Finally, in view of the reasons which cause allocation of empty container, put forwards several countermeasures to reduce the cost of empty container allocation.
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