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Banca de DEFESA: HENRIQUE LIMA WERNECK

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : HENRIQUE LIMA WERNECK
DATA : 06/05/2022
HORA: 09:00
LOCAL: remoto
TÍTULO:

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


PÁGINAS: 100
RESUMO:

Deep reinforcement learning is a method 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 such 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, one learning from edited 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 algorithm 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 fundamental skills.


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
Membro Suplente - Examinador(a) Externo à Instituição - FLAVIO TONIDANDEL - FEI
Notícia cadastrada em: 07/04/2022 17:39
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