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 | Archive PDF | Plain Text | 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. |