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..
Mastering Atari Games with Limited Data
Authors: Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method achieves 190.4% mean human performance and 116.0% median performance on the Atari 100k benchmark with only two hours of real-time game experience and outperforms the state SAC in some tasks on the DMControl 100k benchmark. |
| Researcher Affiliation | Academia | Weirui Ye Shaohuai Liu Thanard Kurutach Pieter Abbeel Yang Gao Tsinghua University, UC Berkeley, Shanghai Qi Zhi Institute EMAIL, EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We implement our algorithm in an easy-to-understand manner and it is available at https://github.com/Ye WR/Efficient Zero. |
| Open Datasets | Yes | More specifically, we use the Atari 100k benchmark. Intuitively, this benchmark asks the agent to learn to play Atari games within two hours of real-world game time. |
| Dataset Splits | Yes | We split this dataset into a training set and a validation set. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies used in the experiments. |
| Experiment Setup | Yes | The benchmark allows the agent to interact with 100 thousand environment steps, i.e. 400 thousand frames due to a frameskip of 4, with each environment. |