4 papers · 1 filter
Duality Theory for Non-Markovian Linear Gaussian Models
Aditya Kudre, Heng-Sheng Chang, Prashant G. Mehta
This work develops a duality theory for partially observed linear Gaussian models in discrete time. The state process evolves according to a causal but non-Markovian (or higher-ord…
Interacting Particle Systems for Fast Linear Quadratic RL
Anant A Joshi, Heng-Sheng Chang, Amirhossein Taghvaei +2
This paper is concerned with the design of algorithms based on systems of interacting particles to represent, approximate, and learn the optimal control law for reinforcement learn…
Error Analysis of Sampling Algorithms for Approximating Stochastic Optimal Control
Anant A. Joshi, Amirhossein Taghvaei, Prashant G. Mehta
This paper is concerned with the error analysis of two types of sampling algorithms, namely model predictive path integral (MPPI) and an interacting particle system (\IPS) algorith…
How to implement the Bayes' formula in the age of ML?
Amirhossein Taghvaei, Prashant G. Mehta
This chapter contains a self-contained introduction to the significance of Bayes' formula in the context of nonlinear filtering problems. Both discrete-time and continuous-time set…