Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
classification
💻 cs.LG
stat.ML
keywords
varianceboundsenvironmentsestimatorgradientnoisepolicyaction
read the original abstract
We study the variance of the REINFORCE policy gradient estimator in environments with continuous state and action spaces, linear dynamics, quadratic cost, and Gaussian noise. These simple environments allow us to derive bounds on the estimator variance in terms of the environment and noise parameters. We compare the predictions of our bounds to the empirical variance in simulation experiments.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.