71 citations · 78 across the 3 of their papers we have counts for
4 papers
Fast Reinforcement Learning with Incremental Gaussian Mixture Models
Rafael Pinto
This work presents a novel algorithm that integrates a data-efficient function approximator with reinforcement learning in continuous state spaces. An online and incremental algori…
Model-Free Episodic Control with State Aggregation
Rafael Pinto
Episodic control provides a highly sample-efficient method for reinforcement learning while enforcing high memory and computational requirements. This work proposes a simple heuris…
Scalable and Incremental Learning of Gaussian Mixture Models
Rafael Pinto, Paulo Engel
This work presents a fast and scalable algorithm for incremental learning of Gaussian mixture models. By performing rank-one updates on its precision matrices and determinants, its…
A Fast Incremental Gaussian Mixture Model
Rafael Pinto, Paulo Engel
This work builds upon previous efforts in online incremental learning, namely the Incremental Gaussian Mixture Network (IGMN). The IGMN is capable of learning from data streams in…