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..
Quantile Credit Assignment
Authors: Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Remi Munos
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show theoretically that this approach gives an unbiased policy gradient estimator that can yield significant variance reductions over a standard value estimate baseline. QCA and HQCA significantly outperform prior state-of-the-art methods on a range of extremely difficult credit assignment problems. |
| Researcher Affiliation | Collaboration | 1Deep Mind 2Harvard University 3Ecole polytechnique. |
| Pseudocode | Yes | Figure 1. Architecture and pseudocode of the QCA algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper describes custom-built environments (Key-To-Door variants, Combinatorial RL) but does not provide access information (URL, DOI, repository) for pre-existing datasets. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts) as it operates on simulated environments generating trajectories. |
| Hardware Specification | No | The paper describes the neural network architectures and training process but does not specify any particular hardware components (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions optimization algorithms (RMSprop) and loss functions but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For High-Variance Key-To-Door, the optimal hyperparameters found for each algorithm can be found in Table 1. |