REINFORCE: policy gradients and variance
A post published on July 12, 2026 derives the REINFORCE estimator without differentiating through the environment and analyzes how variance depends on trajectory length.
Source: Policy Gradients Parte 1: O estimador REINFORCE (fa.bianp.net). Text prepared with AI from this source.
What happened and what to do
Published on July 12, 2026, the post opens a series on policy gradients. It derives the REINFORCE estimator as an unbiased method that does not differentiate through the environment, and analyzes how variance scales with trajectory length. The text highlights properties that help explain the method, without presenting a specific business application.
A team training policies can turn this analysis into an experimentation routine: log rewards, trajectory lengths, and estimate variance; compare configurations; and track training stability in dashboards. This instrumentation can help identify when trajectory length is associated with noisier estimates and guide implementation tests.
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Wendelmaques can diagnose the training pipeline and available data, scope instrumentation, metrics, and dashboards, then implement the solution on the company's infrastructure, including its operation.
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