Reinforcement learning is used to learn adaptive policies for selecting parameters in nonlinear Bayesian filters, improving estimate quality and consistency in experiments with the unscented Kalman filter and stochastic integration filter.
A bayesian framework for reinforcement learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Learning Adaptive Parameter Policies for Nonlinear Bayesian Filtering
Reinforcement learning is used to learn adaptive policies for selecting parameters in nonlinear Bayesian filters, improving estimate quality and consistency in experiments with the unscented Kalman filter and stochastic integration filter.