Banca de QUALIFICAÇÃO: HENRIQUE LIMA WERNECK

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
DISCENTE : HENRIQUE LIMA WERNECK
DATA : 05/11/2020
HORA: 14:00
LOCAL: remoto
TÍTULO:

Entity-Learning with Deep Reinforcement Learning: A Study on Different
Abstractions of Input

PÁGINAS: 100
RESUMO:

Deep reinforcement learning is an algorithm that introduced promising
solutions to various setbacks that reinforcement learning historically
presented. These advances are subject of many recent studies. They
regard, for example, the possibility of automatically abstracting
relevant information from the environment, and performing consistently
using higher dimensional data from complex environments, like entities
present in this environment. For this work, the ViZDoom platform will be
used in order to compare the learning efficiency of distinct input
proposals: one learning from raw image inputs and the other using more
structured data, identifying entities in the scene. The curriculum
learning method adopted for training will gradually increase the
complexity of the environment, validating the performance of the
algorithms at each step. This work aims to contribute with a comparative
study on the learning efficiency displayed by agents when presented to
data with different abstraction levels, learning from a dynamic
environment over ever increasing difficult challenges.

MEMBROS DA BANCA:
Presidente - Interno ao Programa - 2078059 - LUIZ ANTONIO CELIBERTO JUNIOR
Membro Titular - Examinador(a) Interno ao Programa - 1673092 - RONALDO CRISTIANO PRATI
Membro Titular - Examinador(a) Externo à Instituição - REINALDO AUGUSTO DA COSTA BIANCHI - FEI
Membro Suplente - Examinador(a) Interno ao Programa - 1722875 - DAVID CORREA MARTINS JUNIOR