Deep Dive into Policy Gradient Causality Trick and REINFORCE Algorithm

ShawnHymel · x · 2026-08-12

This technical blog explores the causality trick in policy gradients within reinforcement learning.

• The Problem: Traditional policy gradient estimates use the full trajectory return at each update step, leading to high variance and low sample efficiency.
• The Solution: By applying the mathematical concept of causality, the full return is replaced with the discounted return looking forward from the current timestep. This substitution is mathematically sound, effectively reducing variance and significantly improving sample efficiency.
• Algorithm Overview: Building on this derivation, the post details the classic REINFORCE algorithm, a foundational policy gradient method that paved the way for more advanced techniques like Actor-Critic architectures.

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