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Found 10 papers, 6 papers with code
Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments
This work investigates the potential of Reinforcement Learning (RL) to tackle robot motion planning challenges in the dynamic RoboCup Small Size League (SSL).
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning.
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer
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.
An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other.
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer
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.
Deep Categorization with Semi-Supervised Self-Organizing Maps
The results show that Batch SS-SOM is a good option for semi-supervised classification and clustering.
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
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).
MOEA/D with Uniformly Randomly Adaptive Weights
In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/DURAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems.
A Semi-Supervised Self-Organizing Map for Clustering and Classification
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.
A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds
Also, it is important to develop methods that are easy to parameterize in a way that is robust to the different characteristics of the data at hand.
