A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization
Authors: Songtao Lu, Siliang Zeng, Xiaodong Cui, Mark Squillante, Lior Horesh, Brian Kingsbury, Jia Liu, Mingyi Hong
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results tested on multi-agent RL problems showcase the superiority of SLAM compared with the benchmarks. In this section, we evaluate our proposed algorithm using two MARL environments: 1) the cooperative navigation task [16], which is built on the Open AI Gym platform [46]; and 2) the pursuit-evasion game [47], which is built on the Petting Zoo platform [48]. |
| Researcher Affiliation | Collaboration | IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY 10598 songtao@ibm.com,{cuix,mss,lhoresh,bedk}@us.ibm.com Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 {zeng0176,mhong}@umn.edu Dept. of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210 liu@ece.osu.edu |
| Pseudocode | Yes | Algorithm 1 Decentralized implementation of SLAM; Algorithm 2 Decentralized implementation of SLAM-L; Algorithm 3 Decentralized implementation of SLAM-U |
| Open Source Code | No | The paper's checklist explicitly states: 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No]' |
| Open Datasets | Yes | In this section, we evaluate our proposed algorithm using two MARL environments: 1) the cooperative navigation task [16], which is built on the Open AI Gym platform [46]; and 2) the pursuit-evasion game [47], which is built on the Petting Zoo platform [48]. |
| Dataset Splits | No | The paper does not specify exact train/validation/test splits, percentages, or sample counts in the main text. It defers 'Detailed experimental settings' to the supplement. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computing clusters used for the experiments in the main text. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper states: 'Detailed experimental settings and additional numerical results are provided in the supplement.' While it mentions some high-level details like the number of agents and network types, it does not provide concrete hyperparameters (e.g., learning rates, epochs, optimizer settings) in the main text. |