What is recorded here?
Presented the REINFORCE class of stochastic reinforcement-learning algorithms and showed that their weight updates follow the gradient of expected reinforcement under the paper’s stated settings.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the sole-author 1992 Machine Learning article. Earlier actor–critic work, Q-learning, later policy-gradient estimators, variance reduction, natural gradients and deep-RL implementations remain separate.
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People and systems
Ronald J. Williams
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Documented countries / regions: United States
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English catalogue narrative
Williams: REINFORCE policy-gradient algorithms
Presented the REINFORCE class of stochastic reinforcement-learning algorithms and showed that their weight updates follow the gradient of expected reinforcement under the paper’s stated settings.
Scientific scope
The record is the sole-author 1992 Machine Learning article. Earlier actor–critic work, Q-learning, later policy-gradient estimators, variance reduction, natural gradients and deep-RL implementations remain separate.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Ronald J. Williams
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.