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Found 13 papers, 0 papers with code
Dual Ensemble Kalman Filter for Stochastic Optimal Control
In this paper, stochastic optimal control problems in continuous time and space are considered.
Neural Models and Algorithms for Sensorimotor Control of an Octopus Arm
In this article, a biophysically realistic model of a soft octopus arm with internal musculature is presented.
A Survey of Feedback Particle Filter and related Controlled Interacting Particle Systems (CIPS)
In this survey, we describe controlled interacting particle systems (CIPS) to approximate the solution of the optimal filtering and the optimal control problems.
Modeling the Neuromuscular Control System of an Octopus Arm
The octopus arm is a neuromechanical system that involves a complex interplay between peripheral nervous system (PNS) and arm musculature.
Energy Shaping Control of a Muscular Octopus Arm Moving in Three Dimensions
Key contributions of this paper are: (i) modeling of major muscle groups to elicit three-dimensional movements; (ii) a mathematical formulation for muscle activations based on a stored energy function; and (iii) a computationally efficient procedure to design task-specific equilibrium configurations, obtained by solving an optimization problem in the Special Euclidean group SE(3).
A Sensory Feedback Control Law for Octopus Arm Movements
The main contribution of this paper is a novel sensory feedback control law for an octopus arm.
Controlled Interacting Particle Algorithms for Simulation-based Reinforcement Learning
This paper is concerned with optimal control problems for control systems in continuous time, and interacting particle system methods designed to construct approximate control solutions.
Deep FPF: Gain function approximation in high-dimensional setting
The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian.
Convex Q-Learning, Part 1: Deterministic Optimal Control
It is shown that in fact the algorithms are very different: while convex Q-learning solves a convex program that approximates the Bellman equation, theory for DQN is no stronger than for Watkins' algorithm with function approximation: (a) it is shown that both seek solutions to the same fixed point equation, and (b) the ODE approximations for the two algorithms coincide, and little is known about the stability of this ODE.
An Optimal Transport Formulation of the Ensemble Kalman Filter
For this algorithm, the equations for empirical mean and covariance are derived and shown to be identical to the Kalman filter.
Accelerated Flow for Probability Distributions
al. 2016) from vector valued variables to probability distributions.
Accelerated Gradient Flow for Probability Distributions
In particular, we extend the recent variational formulation of accelerated gradient methods in wibisono2016 from vector valued variables to probability distributions.
How regularization affects the critical points in linear networks
The formulation is used to provide a complete characterization of the critical points in terms of the solutions of a nonlinear matrix-valued equation, referred to as the characteristic equation.
