2 citations · 2 across the 2 of their papers we have counts for
8 papers
Deep Stochastic Optimal Control Policies for Planetary Soft-landing
Marcus A. Pereira, Camilo A. Duarte, Ioannis Exarchos +1
In this paper, we introduce a novel deep learning based solution to the Powered-Descent Guidance (PDG) problem, grounded in principles of nonlinear Stochastic Optimal Control (SOC)…
Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEs
Marcus Aloysius Pereira, Ziyi Wang, Ioannis Exarchos +1
This paper introduces a new formulation for stochastic optimal control and stochastic dynamic optimization that ensures safety with respect to state and control constraints. The pr…
NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-End Learning and Control
Ioannis Exarchos, Marcus A. Pereira, Ziyi Wang +1
In this work we propose the use of adaptive stochastic search as a building block for general, non-convex optimization operations within deep neural network architectures. Specific…
Variational Optimization Based Reinforcement Learning for Infinite Dimensional Stochastic Systems
Ethan N. Evans, Marcus A. Pereira, George I. Boutselis +1
Systems involving Partial Differential Equations (PDEs) have recently become more popular among the machine learning community. However prior methods usually treat infinite dimensi…
Deep Forward-Backward SDEs for Min-max Control
Ziyi Wang, Keuntaek Lee, Marcus A. Pereira +2
This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems. The proposed approach relies on the nonlinear Feynman-Kac theorem tha…
Deep 2FBSDEs For Systems With Control Multiplicative Noise
Marcus A Pereira, Ziyi Wang, Tianrong Chen +2
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellmanpartial differen…