5 citations · 17 across the 13 of their papers we have counts for
7 papers · 1 filter
An Optimal Transport Formulation of the Ensemble Kalman Filter
Amirhossein Taghvaei, Prashant G. Mehta
Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF) are numerical algorithms to approximate the solution of the…
Bio-inspired Learning of Sensorimotor Control for Locomotion
Tixian Wang, Amirhossein Taghvaei, Prashant G. Mehta
This paper presents a bio-inspired central pattern generator (CPG)-type architecture for learning optimal maneuvering control of periodic locomotory gaits. The architecture is pres…
Q-learning for POMDP: An application to learning locomotion gaits
Tixian Wang, Amirhossein Taghvaei, Prashant G. Mehta
This paper presents a Q-learning framework for learning optimal locomotion gaits in robotic systems modeled as coupled rigid bodies. Inspired by prevalence of periodic gaits in bio…
A Dual Characterization of Observability for Stochastic Systems
Jin W. Kim, Prashant G. Mehta
This paper is concerned with a characterization of the observability for a continuous-time hidden Markov model where the state evolves as a general continuous-time Markov process a…
Optimal Rate of Convergence for Quasi-Stochastic Approximation
Andrey Bernstein, Yue Chen, Marcello Colombino +3
The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic frameworks for reinforcement learning (RL), and often an efficient approach to solving (or…
What is the Lagrangian for Nonlinear Filtering?
Jin W. Kim, Prashant G. Mehta, Sean P. Meyn
Duality between estimation and optimal control is a problem of rich historical significance. The first duality principle appears in the seminal paper of Kalman-Bucy, where the prob…