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..
DAC: The Double Actor-Critic Architecture for Learning Options
Authors: Shangtong Zhang, Shimon Whiteson
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an empirical study on challenging robot simulation tasks. |
| Researcher Affiliation | Academia | Shangtong Zhang, Shimon Whiteson Department of Computer Science University of Oxford EMAIL |
| Pseudocode | Yes | The pseudocode of DAC is provided in the supplementary materials. |
| Open Source Code | Yes | All implementations are made publicly available 1. https://github.com/ShangtongZhang/DeepRL |
| Open Datasets | Yes | We consider four robot simulation tasks used by Smith et al. (2018) from Open AI gym (Brockman et al., 2016). ... We use 6 pairs of tasks from Deep Mind Control Suite (DMControl, Tassa et al. 2018) |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits. It uses reinforcement learning environments (OpenAI Gym, DeepMind Control Suite) where data is generated through interaction. |
| Hardware Specification | No | The Acknowledgments section mentions 'a generous equipment grant from NVIDIA' but does not specify the exact GPU models, CPU models, or other hardware specifications used for experiments. |
| Software Dependencies | No | The paper mentions using PPO, A2C, OpenAI Gym, and DeepMind Control Suite, but does not provide specific version numbers for these software dependencies or any other libraries. |
| Experiment Setup | Yes | Our PPO implementation uses the same architecture and hyperparameters reported by Schulman et al. (2017). ... We use 4 options for all algorithms, following Smith et al. (2018). We report the online training episode return, smoothed by a sliding window of size 20. |