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 Weighted Path Consistency for Mastering Atari Games
Authors: Dengwei Zhao, Shikui Tu, Lei Xu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted on the Atari 100k benchmark with 26 games and GW-PCZero achieves 198% mean human performance, higher than the state-of-the-art Efficient Zero s 194%, while consuming only 25% of the computational resources consumed by Efficient Zero. |
| Researcher Affiliation | Academia | Dengwei Zhao Shanghai Jiao Tong University EMAIL Shikui Tu Shanghai Jiao Tong University EMAIL Lei Xu Shanghai Jiao Tong University Guangdong Institute of Intelligence Science and Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: Sample Preparation for GW-PCZero; Algorithm 2: Weighted PC target t P C estimation |
| Open Source Code | Yes | 1The source code is available at https://github.com/CMACH508/GW_PCZero. |
| Open Datasets | Yes | Experiments are conducted on the Atari 100k benchmark with 26 games to evaluate GW-PCZero in diverse environments. |
| Dataset Splits | No | The paper refers to the 'Atari 100k benchmark' and '100k interaction steps' but does not specify explicit dataset splits (e.g., percentages or counts) for training, validation, or testing. |
| Hardware Specification | Yes | Experiments are conducted on 4 NVIDIA Tesla A100 GPUs with 16 CPU cores. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We set cb = 1.0 and ca = 0.1 in Eq. (16). Totally 32 of different random seeds are used. Other hyperparameter settings are the same as Efficient Zero, as summarized in Appendix 5. |