Neural Foundations · 1992

Simple Statistical Gradient-Following Algorithms (REINFORCE)

Ronald J. Williams

Introduced REINFORCE, a family of algorithms that adjust a policy's parameters directly along an unbiased estimate of the reward gradient, enabling reinforcement learning in networks that output probabilities over actions.

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Plain-language summary

Williams derived how to nudge the weights of a stochastic network so that actions leading to higher reward become more likely, using only the reward signal and the probability of the chosen action. The estimate of the gradient is unbiased and needs no model of the environment. This policy-gradient approach became the basis for later actor-critic and deep policy-optimization methods used to train agents and language models.

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Record ID: A-031
Record created: 2026-07-13
Last reviewed: 2026-07-14
Record version: 2

https://link.springer.com/article/10.1007/BF00992696

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