The Twisted-Path Particle Filter parameterizes twisting functions via neural networks and optimizes them against a path-measure KL divergence to improve continuous-time particle filtering.
On Bellman equations for continuous-time policy eval- uation i: discretization and approximation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Derives quantitative convergence rates for the gap between optimal policies from regularized discrete-time Bellman equations and true optimal controls in underlying continuous-time stochastic problems.
citing papers explorer
-
Guidance for twisted particle filter: a continuous-time perspective
The Twisted-Path Particle Filter parameterizes twisting functions via neural networks and optimizes them against a path-measure KL divergence to improve continuous-time particle filtering.
-
Discretization error from regularized Reinforcement Learning to continuous-time stochastic control
Derives quantitative convergence rates for the gap between optimal policies from regularized discrete-time Bellman equations and true optimal controls in underlying continuous-time stochastic problems.