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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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“As one of the earliest policy gradient algorithms, REINFORCE (Williams, 1992) provides the foundational theory for training stochastic policies.”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Williams, 1992
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What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions
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In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
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Ad Headline Generation using Self-Critical Masked Language Model
Kanungo, Y. S., Negi, S., & Rajan, A. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers · doi:10.18653/v1/2021.naacl-industry.33 · Cited by 15
In-context Reinforcement Learning with Algorithm Distillation
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Hidden Incentives for Auto-Induced Distributional Shift
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Interference and Generalization in Temporal Difference Learning
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A reinforcement-learning approach to efficient communication
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Adaptive Methods in Machine Learning
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Variance Networks: When Expectation Does Not Meet Your Expectations
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Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning
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Zhao, P., Xiao, K., Zhang, Y., Bian, K., & Yan, W. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/290 · Cited by 7
Mutual influence between language and perception in multi-agent communication games
Ohmer, X., Marino, M., Franke, M., & König, P. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010658 · Cited by 6
“The agents minimize the negative expected reward, − E [ r ] , and their trainable weights are updated using REINFORCE [47], which is a basic policy gradient algorithm.”
Anastassacos, N., Hailes, S., & Musolesi, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6190 · Cited by 4
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
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Language Agents as Optimizable Graphs
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Metadata
- Authors
- Williams, R. J.
- Venue
- Machine Learning
- Published
- 1992-05
- Volume
- 8
- Issue
- 3-4
- Pages
- 229-256
- ISSN
- 0885-6125
- 1573-0565
- Cited by
- 3,416
- References
- 4
- Language
- en
- Licence
- www.springer.com/tdm (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
