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 | Conference PDF | Archive PDF | Plain Text | 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. |