Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
Authors: Xinran Li, Ling Pan, Jun Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations across extensive environments, including multi-agent particle environment, multi-agent Mu Jo Co and Star Craft multi-agent challenge v2, demonstrate the superior performance of Kaleidoscope compared with existing parameter sharing approaches, showcasing its potential for performance enhancement in MARL. |
| Researcher Affiliation | Academia | Xinran Li Ling Pan Jun Zhang Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology xinran.li@connect.ust.hk, lingpan@ust.hk, eejzhang@ust.hk |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. The technical details are described through prose and mathematical formulas. |
| Open Source Code | Yes | The code is publicly available at https://github.com/LXXXXR/Kaleidoscope. |
| Open Datasets | Yes | We test our proposed Kaleidoscope on three benchmark tasks: MPE (Lowe et al., 2017), Ma Mu Jo Co (Peng et al., 2021) and SMACv2 (Ellis et al., 2024). |
| Dataset Splits | No | The paper mentions using multiple random seeds for statistical robustness and reporting 95% confidence intervals, but it does not specify explicit training/validation/test dataset splits with percentages or counts for a single dataset. |
| Hardware Specification | Yes | The experiments on the SMACv2 benchmark were conducted using NVIDIA Ge Force RTX 3090 GPUs, while the experiments on other benchmarks were performed using NVIDIA Ge Force RTX 3080 GPUs. |
| Software Dependencies | No | The paper mentions the use of existing codebases (HARL, EPy MARL, Py MARL2) but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | Hyperparameters To ensure a fair comparison, we implement our method and all the baselines using the same codebase with the same set of hyperparameters, with the exception of method-specific ones. The common hyperparameters are listed in Tables 3 to 5. The Kaleidoscope-specific hyperparameters are provided in Table 6. |