GMAC: A Distributional Perspective on Actor-Critic Framework
Authors: Daniel W Nam, Younghoon Kim, Chan Y Park
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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 <dwtnam@kc-ml2.com>. |
| 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). |