Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
Authors: Robby Costales, Stefanos Nikolaidis
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 29,561 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,408 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results showcase DIVA s unique ability to overcome complex parameterizations and successfully train adaptive agent behavior, far outperforming competitive baselines from prior literature. (from abstract) and 5 Empirical results (section title). |
| Researcher Affiliation | Academia | Robby Costales Stefanos Nikolaidis Department of Computer Science University of Southern California Correspondence to EMAIL. |
| Pseudocode | Yes | Appendix A Algorithmic details. Algorithm 1 DIVA, Algorithm 2 DIVA (detailed), Algorithm 3 QD update. |
| Open Source Code | Yes | Our code is available at https://github.com/robbycostales/diva. |
| Open Datasets | No | The paper utilizes modified versions of environments like GRIDNAV, ALCHEMY [18], and RACING [17]. While these environments are referenced with citations to papers describing them, the paper does not provide direct links, DOIs, or specific repository names for publicly available datasets used in the experiments. It describes them as domains for their experiments rather than external, accessible datasets. |
| Dataset Splits | No | The paper describes training processes for agents and evaluation over environment distributions but does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts) for the data used in the experiments. It details meta-training and evaluation, but not data partitioning. |
| Hardware Specification | Yes | All results were produced on a handful of Titan X or Xp GPUs. |
| Software Dependencies | No | The paper mentions specific libraries and codebases used, such as 'pyribs', 'Vari BAD', 'PLR', and 'ACCEL', but does not specify their version numbers. |
| Experiment Setup | Yes | Table 4: DIVA hyperparameter settings. Table 5: Vari BAD hyperparameter settings. |