Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training
Authors: Pihe Hu, Shaolong Li, Zhuoran Li, Ling Pan, Longbo Huang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experimental investigation across various value-based MARL algorithms on multiple benchmarks demonstrates, for the first time, significant reductions in redundancy of up to 20 in Floating Point Operations (FLOPs) for both training and inference, with less than 3% performance degradation. |
| Researcher Affiliation | Academia | Pihe Hu Tsinghua University Beijing, China hupihe@gmail.com Shaolong Li Central South University Changsha, China shaolongli16@gmail.com Zhuoran Li Tsinghua University Beijing, China lizr20@mails.tsinghua.edu.cn Ling Pan Hong Kong University of Science and Technology Hong Kong, China lingpan@ust.hk Longbo Huang Tsinghua University Beijing, China longbohuang@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 Topology Evoltion[Evci et al., 2020]; Algorithm 2 MAST-QMIX; Algorithm 3 MAST-(OW)QMIX |
| Open Source Code | No | The code will be open-sourced upon publication of the paper. |
| Open Datasets | Yes | In this section, we conduct a comprehensive performance evaluation of MAST across various tasks in the Star Craft Multi-Agent Challenge (SMAC) [Samvelyan et al., 2019] benchmark. Additional experiments on the multi-agent Mu Jo Co (MAMu Jo Co) [Peng et al., 2021] benchmark are provided in Appendix B.9. |
| Dataset Splits | Yes | In this section, we conduct a comprehensive performance evaluation of MAST across various tasks in the Star Craft Multi-Agent Challenge (SMAC) [Samvelyan et al., 2019] benchmark. [...] The environments are tested using their default configurations, with other settings following FACMAC [Peng et al., 2021]. |
| Hardware Specification | Yes | Our experiments are implemented with Py Torch 2.0.0 [Paszke et al., 2017] and run on 4 NVIDIA GTX Titan X (Pascal) GPUs. |
| Software Dependencies | Yes | Our experiments are implemented with Py Torch 2.0.0 [Paszke et al., 2017] |
| Experiment Setup | Yes | Table 3 provides a comprehensive overview of the hyperparameters employed in our experiments for MAST-QMIX, MAST-WQMIX, and MAST-RES. It includes detailed specifications for network parameters, RL parameters, and topology evolution parameters, allowing for a thorough understanding of our configurations. |