ArticleAdvances in neural information processing systems2021
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model.
Article in Advances in neural information processing systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Yield-graph: multi-stage growth-aware maize yield prediction via graph neural networks.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning.Transactions on machine learning research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The curse of dimensionality is a widely known issue in reinforcement learning (RL). In the tabular setting where the state space
Indexed as
Identifiers
36168331PMC9512142What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.