Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov ...

A computational study combines q-rung picture fuzzy arithmetic with dynamic programming to optimize multi-stage decisions ...

Dynamic programming is a methodological framework for solving optimisation problems that evolve over time by breaking them into simpler subproblems. Central to this approach is the principle of ...

This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...

A new reinforcement learning optimizer called LIGHT uses dynamic graph attention and logarithmic reward shaping to cut query ...

We present a method to estimate the capacity value of storage. Our method uses a dynamic program to model the effect of power system outages on the operation and state of charge of storage in ...

Understand the principles of efficient algorithms for dealing with large scale data sets and be able to select appropriate algorithms for specific problems. Understand and be able to apply the main ...

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization.

You could say reactive programming is like functional programming with superpowers. Let's take a look at this dynamic programming style. Reactive programming is an important facet of modern software ...