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..
Subequivariant Graph Reinforcement Learning in 3D Environments
Authors: Runfa Chen, Jiaqi Han, Fuchun Sun, Wenbing Huang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate the proposed method on the proposed benchmarks, where our method consistently and significantly outperforms existing approaches on single-task, multi-task, and zero-shot generalization scenarios. Extensive ablations are also conducted to verify our design. |
| Researcher Affiliation | Collaboration | 1Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua University 2THU-Bosch JCML Center 3Gaoling School of Artificial Intelligence, Renmin University of China 4Beijing Key Laboratory of Big Data Management and Analysis Methods. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and videos are available on our project page: https://alpc91.github.io/SGRL/. and Our codes are available on https://github.com/alpc91/SGRL. |
| Open Datasets | Yes | The environments in our 3D-SGRL are modified from the default 2D-planar setups in Mu Jo Co (Todorov et al., 2012). Specifically, we extend agents in environments including Hopper, Walker, Humanoid and Cheetah (Huang et al., 2020) into 3D counterparts. |
| Dataset Splits | No | We keep 20% of the variants as the zero-shot testing set and use the rest for training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions "Py Torch" and "Mu Jo Co" as software used, but does not provide specific version numbers for these or any other key software dependencies. |
| Experiment Setup | Yes | Table 6 provides the hyperparameters needed to replicate our experiments. Table 6 lists hyperparameters such as "Learning rate 0.0001", "Mini-batch size 100", and "Total attention layers 3". |