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..
Trust Region Evolution Strategies
Authors: Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu4352-4359
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate the effectiveness of TRES on a range of popular Mu Jo Co locomotion tasks in the Open AI Gym, achieving better performance than ES algorithm. |
| Researcher Affiliation | Collaboration | Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu University of Science and Technology of China Microsoft Research Institute of Computing Technology, Chinese Academy of Sciences EMAIL; EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1 Trust Region Evolution Strategies |
| Open Source Code | No | Not found. The paper does not provide explicit statements about the release of its own source code or links to a repository. |
| Open Datasets | Yes | To demonstrate the effectiveness of TRES, we conducted experiments on the continuous Mu Jo Co locomotion tasks from the Open AI Gym (Brockman et al. 2016). |
| Dataset Splits | No | Not found. The paper describes training and evaluation but does not specify explicit training/validation/test dataset splits by percentage or sample count. |
| Hardware Specification | No | Not found. The paper does not specify the exact hardware (e.g., specific CPU or GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | Not found. The paper mentions software like 'Open AI evolution-strategies-starter code' and 'Open AI Gym' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Input: noise standard deviation σ, clip factor λ, epoch number K, learning rate α... We conducted several experiments to investigate their impact. (λ and K)... We observe that K = 15 can gain best performance. |