The authors prove the generalized Hopf formula under mild conditions and use it with SPDEs and the Pontryagin principle to compute curse-of-dimensionality-free dual bounds for stochastic control.
Solving time-continuous stochastic optimal control prob- lems: Algorithm design and convergence analysis of actor-critic flow
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
A deep policy iteration method reformulates finite-horizon mean-field games as regenerative problems with deterministic cycles, using particle systems and one-step updates to handle dimensions up to 10,000 efficiently.
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.
citing papers explorer
-
Dual Approaches to Stochastic Control via SPDEs and the Pathwise Hopf Formula
The authors prove the generalized Hopf formula under mild conditions and use it with SPDEs and the Pontryagin principle to compute curse-of-dimensionality-free dual bounds for stochastic control.
-
Deep Policy Iteration for High-Dimensional Mean-Field Games with Regenerative Reformulation
A deep policy iteration method reformulates finite-horizon mean-field games as regenerative problems with deterministic cycles, using particle systems and one-step updates to handle dimensions up to 10,000 efficiently.
-
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.