4 citations · 4 across the 7 of their papers we have counts for
3 papers · 1 filter
A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning
Sergio Rozada, Santiago Paternain, Juan Andres Bazerque +1
In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acqui…
Transfer Learning for a Class of Cascade Dynamical Systems
Shima Rabiei, Sandipan Mishra, Santiago Paternain
This work considers the problem of transfer learning in the context of reinforcement learning. Specifically, we consider training a policy in a reduced order system and deploying i…
Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
Sergio Rozada, Santiago Paternain, Antonio G. Marques
Value-function (VF) approximation is a central problem in Reinforcement Learning (RL). Classical non-parametric VF estimation suffers from the curse of dimensionality. As a result,…