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..
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning
Authors: Yutong Wang, Ke Xue, Chao Qian
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various (i.e., deceptive and multi-modal) continuous control tasks, show the superior performance of EDO-CS over previous methods, i.e., EDO-CS can achieve a set of policies with both high quality and diversity efficiently while previous methods cannot. |
| Researcher Affiliation | Academia | Yutong Wang , Ke Xue and Chao Qian State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China EMAIL |
| Pseudocode | Yes | Algorithm 1: EDO-CS |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | Yes | To examine the performance of EDO-CS, we conduct experiments on a variety of continuous control tasks from Open AI Gym library (Brockman et al., 2016). |
| Dataset Splits | No | The paper mentions training and testing environments but does not explicitly describe a validation dataset split or its usage. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions 'Open AI Gym library (Brockman et al., 2016)' and 'Mu Jo Co environments' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For EDO-CS, the number M of candidates for selection in each cluster is set to 2, and the arms of the bandit are {λ(1) = 0, λ(2) = 0.5}. Other parameter settings can be found in Appendix A.1. (Appendix A.1 provides tables with specific numerical values for Population size M, Archive size l, Number T of updating iterations, σ in ES, and η in ES for different environments.) |