The cross-entropy method for estimation

93 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Benchmarking Model-Based Reinforcement Learning (2019, methodology): “[4] Zdravko I Botev, Dirk P Kroese, Reuven Y Rubinstein, and Pierre L’Ecuyer.”
Deep Dynamics Models for Learning Dexterous Manipulation (2019, methodology): “Iterative Random-Shooting with Refinement: To address these issues, much prior work [31] has instead taken a cross-entropy method (CEM) approach, which begins as the random shooting approach, but then does this sampling for multiple iterations m ∈ {0 .”
Exploring Model-based Planning with Policy Networks (2019, methodology): “In PETS [5], the authors further use cross entropy method (CEM) [6, 1] to re-samples sequences near the best sequences from the last CEM iteration.”
An Online Learning Approach to Model Predictive Control (2019, background): “he large number of rejected samples. Because of this, in practice, the threshold is set adaptively, e.g., as the largest cost of the top elite fraction of the sampled trajectories with smallest costs [6]. This allows the controller to make the best sampled trajectories more likely and therefore impr”