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..
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning
Authors: Yiqun Chen, Hangyu Mao, Jiaxin Mao, Shiguang Wu, Tianle Zhang, Bin Zhang, Wei Yang, Hongxing Chang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | PTDE can be seamlessly integrated with state-of-the-art algorithms, leading to notable performance enhancements across diverse benchmarks, including the SMAC benchmark, Google Research Football (GRF) benchmark, and Learning to Rank (LTR) task. |
| Researcher Affiliation | Collaboration | 1Renmin University of China 2Sense Time 3Noah s Ark Lab, Huawei 4JD Explore Academy 5Institute of Automation,Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: The first training stage of PTDE |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We conducted training and testing on 10,000 queries (7:3 partition) from the MSLR-WEB30K [Qin and Liu, 2013] dataset |
| Dataset Splits | Yes | We conducted training and testing on 10,000 queries (7:3 partition) from the MSLR-WEB30K [Qin and Liu, 2013] dataset |
| Hardware Specification | No | The paper mentions using '8 parallel runners' but does not provide specific details about the GPU/CPU models, memory, or other hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using Py MARL2 framework [Hu et al., 2021] but does not specify its version or the versions of other key software components like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Details regarding hyperparameters are available in Table 7 in the Appendix. |