A Model-Predictive Motion Planner for the IARA Autonomous Car

We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of eventual obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).Comment: This is a preprint. Accepted by 2017 IEEE International Conference on Robotics and Automation (ICRA

Experience Sharing Between Cooperative Reinforcement Learning Agents

The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task.Comment: Published at the Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligenc

Accelerating learning in multiagent domains through experience sharing

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.Essa dissertação contribui para o crescente campo de inteligência artificial e aprendizado de máquina. Aprendizado é um componente essencial do comportamento humano, a faculdade por trás da nossa habilidade de se adaptar. E essa característica única que diferencia seres humanos de outras espécies, e nos permitiu perserverar e dominar o mundo como nos conhecemos. Através de algoritmos de aprendizado, nós buscamos imbuir agentes artificiais com essa mesma capacidade, para que eles possam aprender e se adaptar interagindo com o ambiente, conseguindo desta forma aumentar seu potencial de atingir seus objetivos. Nesse trabalho, nós buscamos resolver o problema de como múltiplos agentes cooperativos aprendendo concomitantemente podem se beneficar de conhecimento compartilhado entre eles. A habilidade de compartilhar conhecimento adquirido, seja instantaneamente ou através de gerações, é peça chave para a nossa evolução. Segue que o compartilhamento de conhecimento entre agentes autônomos pode ser a chave para acelerar conhecimento em sistemas multiagentes cooperativos. Baseado nesse raciocínio, neste trabalho investigamos métodos de compartilhamento de conhecimento que pode efetivamente levar a uma aceleração no aprendizado. A pesquisa é focada na abordagem de transferência de conhecimento através do compartilhamento de experiências. O modelo MultiAgent Cooperative Experience Sharing (MACES) define uma arquitetura que permite troca de experiências entre agentes cooperativos aprendendo concomitantemente. Neste modelo, investigamos diferentes métodos de compartilhamento de experiências que podem levar a aceleração do aprendizado. O modelo é validado em dois problemas diferentes de aprendizado de reforço, um problema de controle clássico e um de navegação. Os resultados apresentados mostram que o MACES é capaz de reduzir em mais da metade o número de episódios necessários para completar uma tarefa através da cooperação de apenas dois agentes, comparado a agentes não cooperativos. O modelo é aplicável a agentes que implementam métodos de aprendizado de reforço profundo.This dissertation is a contribution to the burgeoning field of artificial intelligence and machine learning. Learning is a core component of human behaviour, the faculty behind our ability to adapt. It is the single characteristic that differentiate humans from other species, and has allowed us to persevere and dominate the world as we know. Through learning algorithms, we seek to imbue artificial agents with the same capacity, so they can as well learn and adapt by interacting with the environment, thus enhancing their potential to achieve their goals. In this work, we address the hard problem of how multiple cooperative agents learning concurrently to achieve a goal can benefit from sharing knowledge with each other. Key to our evolution is our ability to share learned knowledge with each other instantaneously and through generations. It follows that knowledge sharing between autonomous and independent agents could as well become the key to accelerate learning in cooperative multiagent settings. Pursuing this line of inquiry, we investigate methods of knowledge sharing that can effectively lead to faster learning. We focus on the approach of transferring knowledge by experience sharing. The proposed MultiAgent Cooperative Experience Sharing (MACES) model defines an architecture that allows experience sharing between concurrently learning cooperative agents. Within MACES, we investigate different methods of experience sharing that can lead to accelerated learning. The proposed model is validated in two different reinforcement learning settings, a classical control and a navigation problem. The results shows that MACES is able to reduce in over a half the number of episodes required to complete a task through cooperation of only two agents, compared to a single agent baseline. The model is applicable to deep reinforcement learning agents

Aprendizagem em Sociologia: o que discutem as dissertações do ProfSocio (2020-2021)

O presente trabalho teve como objetivo analisar as dissertações da linha “Práticas de ensino e conteúdos curriculares” do Mestrado Profissional de Sociologia em Rede Nacional (ProfSocio) defendidas nos anos de 2020 e 2021. Foram identificados 55 trabalhos, destes foram selecionadas 12 produções que versam sobre metodologia, didática e avaliação da aprendizagem no ensino de Sociologia. A partir disso, observou-se que as pesquisas selecionadas trabalham com diferentes objetos de ensino; a maioria das pesquisas investiga o uso de recursos didáticos nas aulas de Sociologia; a disciplina de Sociologia pode se beneficiar de variadas metodologias de ensino; a produção sobre avaliação da aprendizagem na disciplina de Sociologia ainda é insuficiente; e, por fim, é necessário que questões relativas à aprendizagem em Sociologia sejam pautadas nas agendas de pesquisa do campo do ensino de Sociologia

Polish device for FOCCoS/PFS slit system

The Fiber Optical Cable and Connector System, FOCCoS, for the Prime Focus Spectrograph, PFS, is responsible for transporting light from the Subaru Telescope focal plane to a set of four spectrographs. Each spectrograph will be fed by a convex curved slit with 616 optical fibers organized in a linear arrangement. The slit frontal surface is covered with a special dark composite, made with refractory oxide, which is able to sustain its properties with minimum quantities of abrasives during the polishing process; this stability is obtained This stability is obtained by the detachment of the refractory oxide nanoparticles, which then gently reinforce gently the polishing process and increase its the efficiency. The surface roughness measured in several samples after high performance polishing was about 0.01 microns. Furthermore, the time for obtaining a polished surface with this quality is about 10 times less than the time required for polishing a brass, glass or ceramic surface of the same size. In this paper, we describe the procedure developed for high quality polishing of this type of slit. The cylindrical polishing described here, uses cylindrical concave metal bases on which glass paper is based. The polishing process consists to use grid sequences of 30 microns, 12 microns, 9 microns, 5 microns, 3 microns, 1 micron and, finally, a colloidal silica on a chemical cloth. To obtain the maximum throughput, the surface of the fibers should be polished in such a way that they are optically flat and free from scratches. The optical fibers are inspected with a microscope at all stages of the polishing process to ensure high quality. The efficiency of the process may be improved by using a cylindrical concave composite base as a substrate suitable for diamond liquid solutions. Despite this process being completely by hand, the final result shows a very high quality