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..
Smoothed Action Value Functions for Learning Gaussian Policies
Authors: Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a number of evaluations of Smoothie to compare to DDPG. We choose DDPG as a baseline because it (1) utilizes gradient information of a Q-value approximator, much like the proposed algorithm; and (2) is a standard algorithm well-known to have achieve good, sample-ef๏ฌcient performance on continuous control benchmarks. |
| Researcher Affiliation | Collaboration | 1Google Brain 2Department of Computing Science, University of Alberta. |
| Pseudocode | Yes | Algorithm 1 Smoothie |
| Open Source Code | No | The paper does not include an unambiguous statement of code release or a direct link to a source-code repository for the described methodology. |
| Open Datasets | Yes | We consider standard continuous control benchmarks available on Open AI Gym (Brockman et al., 2016) utilizing the Mu Jo Co environment (Todorov et al., 2012). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like OpenAI Gym and MuJoCo but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For each task we performed a hyperparameter search over actor learning rate, critic learning rate and reward scale... Additional implementation details are provided in the Appendix. |