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..
Generalized Data Distribution Iteration
Authors: Jiajun Fan, Changnan Xiao
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate our state-of-the-art (SOTA) performance on Arcade Learning Environment (ALE), wherein our algorithm has achieved 9620.33% mean human normalized score (HNS), 1146.39% median HNS and surpassed 22 human world records using only 200M training frames. Our performance is comparable to Agent57 s while we consume 500 times less data. |
| Researcher Affiliation | Collaboration | 1Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing, China 2Byte Dance, Beijing, China. |
| Pseudocode | Yes | Algorithm 2 GDI-I3 Algorithm. Algorithm 3 GDI-H3 Algorithm. Algorithm 4 Bandits Controller |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | We evaluated all agents on 57 Atari 2600 games from the arcade learning environment (Bellemare et al., 2013, ALE) |
| Dataset Splits | No | The paper discusses evaluation using multiple environments and seeds, but it does not specify traditional training/validation/test dataset splits with percentages or counts, as is common in supervised learning. The evaluation is typical for reinforcement learning environments where agents interact directly with the game. |
| Hardware Specification | Yes | all the experiment is accomplished using a single CPU with 92 cores and a single Tesla-V100-SXM2-32GB GPU. |
| Software Dependencies | No | The paper mentions software components like 'LSTM' and 'Adam', but it does not provide specific version numbers for any programming languages, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | Table 5. Hyperparameters for Atari experiments. Parameter Value Num. Frames 200M (2E+8) Replay 2 Num. Environments 160 Batch size 64 Discount (γ) 0.997 Optimizer Adam Learning Rate 5e-4 |