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..
Smoothing Advantage Learning
Authors: Yaozhong Gan, Zhe Zhang, Xiaoyang Tan6657-6664
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present our experimental results conducted over six games (Lunarlander; Asterix, Breakout, Space invaders, Seaquest, Freeway) from Gym (Brockman et al. 2016) and Min Atar (Young and Tian 2019). In addition, we also run some experiments on Atari games in Appendix. |
| Researcher Affiliation | Academia | College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics |
| Pseudocode | Yes | Algorithm 1 gives the detailed implementation pipeline in Appendix. |
| Open Source Code | No | The paper does not provide a link to open-source code for the methodology described, nor does it explicitly state that the code is made publicly available. |
| Open Datasets | Yes | In this section, we present our experimental results conducted over six games (Lunarlander; Asterix, Breakout, Space invaders, Seaquest, Freeway) from Gym (Brockman et al. 2016) and Min Atar (Young and Tian 2019). |
| Dataset Splits | No | The paper mentions test procedures but does not specify training, validation, or test dataset splits (e.g., percentages or sample counts) for the environments used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Particularly, we choose α from the set of {0.2, 0.3, 0.5, 0.9} for AL (Bellemare et al. 2016). For SAL, we choose ω and α among {0.2, 0.3, 0.5, 0.9}, but the hyperparameters satisfy α < ω. For Munchausen-DQN (M-DQN) (Vieillard, Pietquin, and Geist 2020), we fix τ = 0.03, choose α from the set of {0.2, 0.3, 0.5, 0.9}. |