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..
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
Authors: Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate this via comprehensive experiments on a set of gridworld and continuous control environments. |
| Researcher Affiliation | Collaboration | 1National University of Singapore 2City University of Hong Kong 3Byte Dance AI lab. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the code for their methodology is open-source or publicly available. |
| Open Datasets | Yes | The gridworld environments are built with Mini Grid (Chevalier-Boisvert et al., 2018) and the continuous control environments are created with Py Bullet (Coumans & Bai, 2016 2019). |
| Dataset Splits | No | The paper mentions collecting trajectories for training and discusses evaluation, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or counts) within the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions environments built with Mini Grid and Py Bullet, and the use of Deep Q-learning, but it does not list specific version numbers for software dependencies such as libraries, frameworks, or programming languages. |
| Experiment Setup | No | The paper mentions that 'More details about training setup can be found in the Appendix' and provides some high-level descriptions of the experimental approach (e.g., 'd = 10 for the dimension of the Laplacian representation', 'learned with Deep Q-learning'), but it does not specify a comprehensive list of hyperparameters or detailed system-level training settings in the main text. |