4 citations · 4 across the 15 of their papers we have counts for
6 papers · 1 filter
Low-Rank Tensors for Multi-Dimensional Markov Models
Madeline Navarro, Sergio Rozada, Antonio G. Marques +1
This work presents a low-rank tensor model for multi-dimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on…
Deterministic Policy Gradient Primal-Dual Methods for Continuous-Space Constrained MDPs
Sergio Rozada, Dongsheng Ding, Antonio G. Marques +1
We study the problem of computing deterministic optimal policies for constrained Markov decision processes (MDPs) with continuous state and action spaces, which are widely encounte…
A Multi-resolution Low-rank Tensor Decomposition
Sergio Rozada, Antonio G. Marques
The (efficient and parsimonious) decomposition of higher-order tensors is a fundamental problem with numerous applications in a variety of fields. Several methods have been propose…
Tensor Low-rank Approximation of Finite-horizon Value Functions
Sergio Rozada, Antonio G. Marques
The goal of reinforcement learning is estimating a policy that maps states to actions and maximizes the cumulative reward of a Markov Decision Process (MDP). This is oftentimes ach…
Matrix Low-Rank Approximation For Policy Gradient Methods
Sergio Rozada, Antonio G. Marques
Estimating a policy that maps states to actions is a central problem in reinforcement learning. Traditionally, policies are inferred from the so called value functions (VFs), but e…
Matrix Low-Rank Trust Region Policy Optimization
Sergio Rozada, Antonio G. Marques
Most methods in reinforcement learning use a Policy Gradient (PG) approach to learn a parametric stochastic policy that maps states to actions. The standard approach is to implemen…