Study and development of reinforcement learning multiagent collaborative algorithms for solving families of problems

Baldazo Escriña, David (2017). Study and development of reinforcement learning multiagent collaborative algorithms for solving families of problems. Proyecto Fin de Carrera / Trabajo Fin de Grado, E.T.S.I. Telecomunicación (UPM), Madrid.

Descripción

Título: Study and development of reinforcement learning multiagent collaborative algorithms for solving families of problems
Autor/es:
  • Baldazo Escriña, David
Director/es:
  • Zazo Bello, Santiago
Tipo de Documento: Proyecto Fin de Carrera/Grado
Grado: Grado en Ingeniería de Tecnologías y Servicios de Telecomunicación
Fecha: 2017
Materias:
Palabras Clave Informales: Artificial intelligence, reinforcement learning, dynamic programming, optimal control, transfer learning, distributed, multiagent, diffusion, Pareto optimality.
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Artificial intelligence algorithms enable autonomous agents to perform sophisticated tasks with great accuracy. A particularly relevant technique within artificial intelligence is that of reinforcement learning, which allows strategic planning and optimal control of an environment through interaction with it and without prior knowledge. Most current algorithms based on reinforcement learning allow us to learn the optimal policy that solves a specific problem, that is, the actions performed in each state that allow maximizing the reward obtained by the agent when it interacts with a given environment. It is of great interest the study of techniques that allow to obtain policies that are suitable not only for a specific problem, but for a whole family of similar problems. This new problem is called transfer learning, where the goal is that the knowledge obtained during the resolution of a problem is useful to solve other different but related problems. The goal of this final degree project is the development of distributed reinforcement learning algorithms in which multiple agents learn different instances of a family of problems, so that each agent learns by interacting with their particular environment, but the agents communicate with each other and cooperate to learn a policy that is good for the whole family of problems. We will also develop techniques to evaluate whether these algorithms generalize in some sense the solution to any similar problem.

Más información

ID de Registro: 47539
Identificador DC: http://oa.upm.es/47539/
Identificador OAI: oai:oa.upm.es:47539
Depositado por: Biblioteca ETSI Telecomunicación
Depositado el: 28 Ago 2017 10:24
Ultima Modificación: 28 Ago 2017 10:24
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