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se presenta el desarrollo de un algoritmo bajo código fuente JavaScript para la solución de la ecuación de flujo crítico (2). Se implementó un lenguaje de programación orientado a objetos para sistemas Android 4.0 o superiores a partir de procesos iterativos e incrementales (Desarrollo ágil). Se utilizó el método numérico de Newton-Raphson para determinar la profundidad crítica de sietes secciones hidráulicas (Rectangular, trapezoidal, trapezoidal asimétrico, triangular, triangular asimétrico, parabólico y circular). Con el propósito de acelerar y garantizar el nivel de convergencia para cada una de las secciones se obtuvo una función potencial para establecer el valor semilla en el proceso iterativo, este valor se asocia de manera directa a las condiciones preestablecidas del problema hidráulico. La aplicación calcula la profundidad crítica, velocidad crítica, área

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Lazaridou., et al 2017 proposed a framework for language learning that relies on multi-agent communication. The agents in the framework were setup in a referential game where they communicated about many images. In this paper, we propose an experiment where agents develop a private language for referring to specified sentences given a set of sentences. The challenge is for the agents to learn a method of distinguishing differences between sentences and to develop a shared language to be able to refer to particular sentences by those distinguishing features. We will evaluate the agents' ability to accurately identify and differentiate the sentences. In addition, we will identify patterns in the methods that the agents develop to refer to the different types of sentences.Keywords: Reinforcement learning, multi-agent coordination

Report without cover page Created by Zengjie Xia on 30/01/2018. Copyright © 2018 Zengjie Xia. All rights reserved.

Template for final project reports for EEC 136AB Electronics Design Project, a senior capstone team-based design course at UC Davis.