Radar ·
REINFORCE: an estimator for policy gradients
Published on July 12, 2026, the first post in a series derives the REINFORCE estimator as an unbiased policy-gradient method and analyzes how variance grows with trajectory length.
The first post in the policy-gradients series derives the REINFORCE estimator, described as unbiased. The derivation does not differentiate through the environment, and the analysis considers how variance scales with trajectory length. The material presents the method as a foundation of policy gradients and highlights an important statistical property.
To check the material, consult the original publication and verify its derivation, assumptions, and variance analysis; the available summary provides no formulas or quantitative results. If you use AI to study or apply the method, avoid sending personal data or internal content: check your organization’s policies and validate explanations against the original text.
Original source: Policy Gradients Parte 1: O estimador REINFORCE