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Due to the high variance of policy gradients, on-policy optimizationalgorithms are plagued with low sample efficiency. In this work, we proposeAugment-Reinforce-Merge (ARM) policy gradient estimator as an unbiasedlow-variance alternative to previous baseline estimators on tasks with binaryaction space, inspired by the recent ARM gradient estimator for discrete randomvariable models. We show that the ARM policy gradient estimator achievesvariance reduction with theoretical guarantees, and leads to significantly morestable and faster convergence of policies parameterized by neural networks.

Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy