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Found 5 papers, 0 papers with code
Decentralized Safe Multi-agent Stochastic Optimal Control using Deep FBSDEs and ADMM
In this work, we propose a novel safe and scalable decentralized solution for multi-agent control in the presence of stochastic disturbances.
Deep $\mathcal{L}^1$ Stochastic Optimal Control Policies for Planetary Soft-landing
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) and Feynman-Kac theory.
NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-End Learning and Control
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.
Deep 2FBSDEs For Systems With Control Multiplicative Noise
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellmanpartial differential equations.
Deep Forward-Backward SDEs for Min-max Control
This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems.
