43 citations · 73 across the 10 of their papers we have counts for
11 papers
Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments
Mateus G. Machado, João G. Melo, Cleber Zanchettin +4
This work investigates the potential of Reinforcement Learning (RL) to tackle robot motion planning challenges in the dynamic RoboCup Small Size League (SSL). Using a heuristic con…
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Shivakanth Sujit, Pedro H. M. Braga, Jorg Bornschein +1
Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challen…
Prioritizing Samples in Reinforcement Learning with Reducible Loss
Shivakanth Sujit, Somjit Nath, Pedro H. M. Braga +1
Most reinforcement learning algorithms take advantage of an experience replay buffer to repeatedly train on samples the agent has observed in the past. Not all samples carry the sa…
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer
Felipe B. Martins, Mateus G. Machado, Hansenclever F. Bassani +2
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 evaluat…
An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
Carlos H. C. Pena, Mateus G. Machado, Mariana S. Barros +6
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 wi…
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer
Hansenclever F. Bassani, Renie A. Delgado, José Nilton de O. Lima Junior +5
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 (VS…
Deep Categorization with Semi-Supervised Self-Organizing Maps
Pedro H. M. Braga, Heitor R. Medeiros, Hansenclever F. Bassani
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. Label…
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
Hansenclever F. Bassani, Renie A. Delgado, Jose Nilton de O. Lima Junior +3
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 t…
MOEA/D with Uniformly Randomly Adaptive Weights
Lucas R. C. de Farias, Pedro H. M. Braga, Hansenclever F. Bassani +1
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…
A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds
Pedro H. M. Braga, Hansenclever F. Bassani
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…
A Semi-Supervised Self-Organizing Map for Clustering and Classification
Pedro H. M. Braga, Hansenclever F. Bassani
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 l…