Action-Dependent Factorized Baselines

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

OpenAI published a research paper on variance reduction techniques for policy gradient methods in reinforcement learning. The work introduces action-dependent factorized baselines as a way to reduce variance in policy gradient estimates without introducing bias. This is a foundational RL training methodology contribution relevant to improving sample efficiency in reinforcement learning.