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