Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exact Reduction of Huge Action Spaces in General Reinforcement Learning
Authors: Sultan J. Majeed, Marcus Hutter8874-8883
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we provide explicit and exact constructions and equivalence proofs for all quantities of interest for arbitrary history-based processes. |
| Researcher Affiliation | Collaboration | Sultan J. Majeed1*, Marcus Hutter2 1,2Research School of Computer Science, ANU, Canberra 2Google Deep Mind, London |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit statement) for open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with specific dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, such as hyperparameter values or training configurations. |