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..
Learning values across many orders of magnitude
Authors: Hado P. van Hasselt, Arthur Guez, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran the Double DQN algorithm [van Hasselt et al., 2016] in three versions: without changes, without clipping both rewards and temporal difference errors, and without clipping but additionally using Pop-Art. |
| Researcher Affiliation | Industry | Google Deep Mind |
| Pseudocode | Yes | Algorithm 1 SGD on squared loss with Pop-Art |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | The Arcade Learning Environment (ALE) [Bellemare et al., 2013] |
| Dataset Splits | No | The paper mentions '200M frames' for training and evaluating 'on 100 episodes', and for binary regression '5000 samples' and 'Every 1000 samples, we present the binary representation of 2^16 - 1'. However, it does not provide specific training, validation, and test dataset splits with percentages or absolute counts for reproducibility. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory amounts, or cloud instance specifications) used for running experiments are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) are provided. |
| Experiment Setup | Yes | We roughly tuned the main step size and the step size for the normalization to 10^-4. |