Pedro Braga

Pedro Braga
Federal University of Pernambuco | UFPE · Department of Computer Science

MSc in Computer Science

About

17
Publications
2,989
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47
Citations
Introduction
I am a Ph.D. Candidate in Computer Science at Centro de Informática - Universidade Federal de Pernambuco (CIn-UFPE). I have been researching Artificial Intelligence during my Masters and currently in my Ph.D.
Additional affiliations
February 2019 - March 2022
Federal University of Pernambuco
Position
  • PhD Student

Publications

Publications (17)
Preprint
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Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods. A known difficulty in applying reinforcement learning to robotics is the high number of experience samples required, being the use of...
Preprint
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The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other. Traditionally, a deterministic coach agent will choose the most suitable strategy and formation for each adversary's strategy. Therefore, the role of a coach is of great importance to the game. In this sense, this p...
Preprint
Full-text available
This article introduces an open framework, called VSSS-RL, for studying Reinforcement Learning (RL) and sim-to-real in robot soccer, focusing on the IEEE Very Small Size Soccer (VSSS) league. We propose a simulated environment in which continuous or discrete control policies can be trained to control the complete behavior of soccer agents and a sim...
Preprint
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Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Labeling is an expensive, time-consuming, and difficult task. It is usually done manually, which collaborates with the incorporation of noise and errors to the data. Hence, it...
Preprint
Full-text available
This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS), a traditional league in the Latin American Robotics Competition (LARC). In the VSSS league, two teams of three small robots play against each other. We propose a simulated environment in which co...
Preprint
Full-text available
Previous research has shown the potential that Deep Neural Networks have in building representations that are useful not only for the task that the network was trained for but also for correlated tasks that take data from similar input distributions. For instance, recent works showed that representations built by a Convolutional Neural Network (CNN...
Preprint
Full-text available
When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Second...
Conference Paper
Full-text available
In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to improve the obtained performance. Also, it is important to develop methods that are eas...
Preprint
Full-text available
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning algorithms can work with both types of data, combining them to obtain better performance for both clustering and classification....
Preprint
Full-text available
In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to improve the obtained performance. Also, it is important to develop methods that are e...
Conference Paper
When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Second...
Conference Paper
Full-text available
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning algorithms can work with both types of data, combining them to obtain better performance for both clustering and classification. A...
Article
Full-text available
Purpose: to present a new application for mobile devices, referred to as Desembaralhando, for intervention in the problem of dyslexic children mirror writring. Methods: the development of the application is the result of a set of clinical and speech therapy information and experiences, which points out frequency of letter mirroring as a challengin...

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