Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization

Maxim Kaledin, Alexander Golubev, Denis Belomestny

Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimat…