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..
Human-Readable Neuro-Fuzzy Networks from Frequent Yet Discernible Patterns in Reward-Based Environments
Authors: John Wesley Hostetter, Adittya Soukarjya Saha, Md Mirajul Islam, Tiffany Barnes, Min Chi
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated on Cart Pole [Barto et al., 1983], Mountain Car [Moore, 1990], and Two-Link Arm [Sutton, 1995]. |
| Researcher Affiliation | Academia | John Wesley Hostetter , Adittya Soukarjya Saha , Md Mirajul Islam , Tiffany Barnes and Min Chi North Carolina State University EMAIL |
| Pseudocode | No | The paper describes the methods in detailed prose and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Sample code demonstrating FYD is public (MIT license) [Hostetter, 2025].1 1https://github.com/john Hostetter/IJCAI-2025-FYD |
| Open Datasets | No | Training data was collected by a DNN using DQL and experience replay while solving the given environment online. |
| Dataset Splits | No | During each run, the amount of data available for offline training gradually increased to show how conditions behave as more data is provided. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware details such as GPU/CPU models, memory, or cloud computing resources used for running the experiments. |
| Software Dependencies | No | All methods were optimized by Adam [Kingma and Ba, 2014]. |
| Experiment Setup | Yes | Shared parameters were identical: α = 1.0, γ = 0.99, learning rate η = 3 10 4, and the batch size was 32. For FYD or CEW, CLIP used κ = 0.2, ϵ = 0.6, and ECM s distance threshold, Dthr, was 0.1. Training data was collected by a DNN using DQL and experience replay while solving the given environment online. |