PMAC: Personalized Multi-Agent Communication
Authors: Xiangrui Meng, Ying Tan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show the strength of personalized communication in a variety of cooperative scenarios. Our approach exhibits competitive performance compared to existing methods while maintaining notable computational efficiency. We empirically evaluate PMAC on a variety of multi-agent cooperative tasks which are hard-level Traffic Junction (hard TJ) (Sukhbaatar, Szlam, and Fergus 2016), Cooperative Navigation (CN), and Predator-Prey (PP) in Multi-agent Particle Environment (MPE) (Mordatch and Abbeel 2017; Lowe et al. 2017), and Google Research Football (GRF) (Kurach et al. 2020). |
| Researcher Affiliation | Academia | Xiangrui Meng1,2, Ying Tan1,2,3,4 * 1 School of Intelligence Science and Technology, Peking University 2 Key Laboratory of Machine Perceptron (MOE), Peking University 3 Institute for Artificial Intelligence, Peking University 4 National Key Laboratory of General Artificial Intelligence mxxxr@stu.pku.edu.cn, ytan@pku.edu.cn |
| Pseudocode | No | The paper does not contain a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of the source code for the described methodology. |
| Open Datasets | Yes | We empirically evaluate PMAC on a variety of multi-agent cooperative tasks which are hard-level Traffic Junction (hard TJ) (Sukhbaatar, Szlam, and Fergus 2016), Cooperative Navigation (CN), and Predator-Prey (PP) in Multi-agent Particle Environment (MPE) (Mordatch and Abbeel 2017; Lowe et al. 2017), and Google Research Football (GRF) (Kurach et al. 2020). |
| Dataset Splits | No | The paper mentions running experiments for a certain number of episodes and over multiple runs (e.g., '1,000,000 episodes', 'standard deviations over 3 runs'), but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts for the environments used. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory, or cloud instance specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We use the hard level TJ task on a 18 × 18 grid (see Fig. 4(a)). The total number of cars at any given time is limited to N = 20. We use actor-critic (Sutton and Barto 2018) to train our model. PMAC demonstrates faster convergence and achieves higher rewards when η = 0.001. |