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..
Regularized Q-Learning
Authors: Han-Dong Lim, Donghwan Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we briefly present the experimental results under well-known environments in Tsitsiklis and Van Roy [1996], Baird [1995], where Q-learning with linear function approximation diverges. As from Figure 2b, our algorithm shows faster convergence rate than other algorithms. |
| Researcher Affiliation | Academia | Han-Dong Lim Electrical Engineering, KAIST EMAIL Donghwan Lee Electrical Engineering, KAIST EMAIL |
| Pseudocode | Yes | The pseudo-code is given in Appendix A.16. |
| Open Source Code | Yes | We have attached the code in the supplementary files. |
| Open Datasets | Yes | In this section, we briefly present the experimental results under well-known environments in Tsitsiklis and Van Roy [1996], Baird [1995], where Q-learning with linear function approximation diverges. |
| Dataset Splits | No | No explicit validation set or train/validation/test splits are mentioned in the paper for reproducing data partitioning. |
| Hardware Specification | No | Our experiments can simply run on normal computer because we do not require any heavy computation including using GPU. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with specific versions) are mentioned in the paper. |
| Experiment Setup | Yes | Learning rate for Greedy GQ (GGQ) and Coupled Q Learning (CQL), which have two learning rates, are set as 0.05 and 0.25, respectively... For Reg Q, we set the learning rate as 0.25, and the weight η as two. |