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..
GMAC: A Distributional Perspective on Actor-Critic Framework
Authors: Daniel W Nam, Younghoon Kim, Chan Y Park
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that GMAC captures the correct representation of value distributions and improves the performance of a conventional actor-critic method with low computational cost, in both discrete and continuous action spaces using Arcade Learning Environment (ALE) and Py Bullet environment. |
| Researcher Affiliation | Industry | 1KC Machine Learning Lab, Seoul, Korea. Correspondence to: Daniel <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 SR(λ) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | in both discrete and continuous action spaces using Arcade Learning Environment (ALE) and Py Bullet environment. |
| Dataset Splits | No | The paper mentions using Arcade Learning Environment (ALE) and Py Bullet environment, but it does not specify exact percentages, sample counts, or explicit references to predefined train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | For a fair comparison, we keep all common hyperparameters consistent across the algorithms except for the value heads and their respective hyperparameters (see Appendix F). |