Machine Learning Applications in High Dimensional Stochastic Control
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 2490
Special Issue Editor
Special Issue Information
Dear Colleagues,
Decision-theoretic planning is naturally formulated and solved using a discrete-time stochastic control framework. This theory provides a fundamental and intuitive formalism not only for sequential decision optimization but also for diverse learning problems. One of the main research areas here deals with the following situation: Starting from a random terminal value of a sequential decision problem, a non-linear recursion computes the value of a strategy at a given time from its expectation one-time step ahead. Such nesting in the calculations causes a deviation from the true value, which is inevitably progressing through all time steps of the backward induction. This error propagation is typically hard to quantify and control, particularly if the dimension of the decision variables is high, causing a variety of problems -- usually referred to as the curse of dimensionality. To deal with the dimension problems, numerous approaches have been suggested, ranging from the discretization of the state space to approximations of functions on this space. These techniques are usually combined with Monte-Carlo methods. However, many important problems still remain computationally infeasible. The proposed special issue of "Algorithms" focuses on a modern development: Unlike in the classical approximate dynamic programming, an optimal policy can be approached using neural networks techniques. This idea benefits from a huge pool of knowledge accumulated in the Machine Learning area and raises the hope that with some work, we soon can address control problems whose dimension is out of reach today.
I cordially invite your contributions to this exciting development.
Dr. Juri Hinz
Guest Editor
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