Introduces MF-PhiBE to perform continuous-time mean-field RL from discrete data, with O(Δt) consistency and O((Δt)^2) accuracy in the LQ case.
On Bellman equations for continuous-time policy eval- uation i: discretization and approximation
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
Introduces a new Q-function definition for continuous-time RL and convergent off-policy algorithms under linear function approximation in model-based and model-free settings.
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.
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
-
Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data
Introduces MF-PhiBE to perform continuous-time mean-field RL from discrete data, with O(Δt) consistency and O((Δt)^2) accuracy in the LQ case.
-
PhiBE-Q-Learning: Bridging Off-Policy Reinforcement Learning and Continuous-Time Control
Introduces a new Q-function definition for continuous-time RL and convergent off-policy algorithms under linear function approximation in model-based and model-free settings.
-
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.