Inverse Attention Agents for Multi-Agent Systems
Authors: Qian Long, Ruoyan Li, Minglu Zhao, Tao Gao, Demetri Terzopoulos
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments in a continuous environment, tackling demanding tasks encompassing cooperation, competition, and a blend of both. They demonstrate that the inverse attention network successfully infers the attention of other agents, and that this information improves agent performance. Additional human experiments show that, compared to baseline agent models, our inverse attention agents exhibit superior cooperation with humans and better emulate human behaviors. |
| Researcher Affiliation | Academia | Qian Long UCLA EMAIL Ruoyan Li UCLA EMAIL Minglu Zhao UCLA EMAIL Tao Gao UCLA EMAIL Demetri Terzopoulos UCLA EMAIL |
| Pseudocode | Yes | Algorithm 1: Inverse Attention Algorithm |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | Yes | We demonstrate the effectiveness of our approach through a series of experiments adapted from the Multi-agent Particles Environment (MPE) (Lowe et al., 2017). All of the environments used in our experiment were built on top of MPE (Mordatch and Abbeel, 2017; Lowe et al., 2017). |
| Dataset Splits | Yes | 70% of the dataset is used for training, 10% of the dataset is split into a validation set, which is used for early stopping, and 20% of the dataset is used for testing. |
| Hardware Specification | Yes | Hardware specifications: All experiments are run on servers/workstations with the following configurations: 128 CPU cores, 692GB RAM; 128 CPU cores, 1.0TB RAM; 32 CPU cores, 120GB RAM; 24 CPU cores, 80GB RAM, 1 NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions several MARL training schemes like MAPPO, IPPO, MAA2C, and To M2C* as baseline methods but does not specify specific version numbers for underlying software libraries (e.g., Python, PyTorch, TensorFlow) that would be needed for replication. |
| Experiment Setup | Yes | Table 7: Hyperparameters for the gradient field; Table 8: Hyperparameters for the agent training; Table 9: Hyperparameters for the inverse network |