CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation
Authors: Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan, Cees G Snoek, Jan-jakob Sonke, Efstratios Gavves
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the Three Dworld Multi-Agent Transport and Communicative Watch-And-Help tasks demonstrate Ca Po s much higher task completion rate and efficiency compared with state-of-the-arts. |
| Researcher Affiliation | Academia | 1University of Amsterdam, The Netherlands 2Singapore Management University, Singapore 3The Netherlands Cancer Institute , The Netherlands 4Archimedes/Athena RC, Greece |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. It describes processes and prompt templates for LLMs, but not structured algorithms for the core methodology. |
| Open Source Code | Yes | The code is released at https://github.com/jliu4ai/Ca Po. |
| Open Datasets | Yes | We follow Co ELA, and adopt the Three Dworld Multi-Agent Transport (TDW-MAT) task (Zhang et al., 2023b), and the Communicative Watch-And-Help (C-WAH) task (Zhang et al., 2023b) to test our Ca Po. |
| Dataset Splits | No | The test set of TDW-MAT consists 24 episodes, which evenly divided into food and stuff tasks. In C-WAH, ... The test set contains 10 episodes, including both symbolic and visual observation settings. The paper mentions the size of the test sets but does not specify training/validation splits or overall dataset partitioning. |
| Hardware Specification | No | The paper mentions using specific LLMs (GPT3.5-turbo, GPT-4, LLAMA-2-13B-CHAT) but does not provide specific hardware details like GPU/CPU models or memory used for running the experiments or the embodied agents themselves. |
| Software Dependencies | No | The paper mentions using GPT3.5-turbo and GPT-4 from the Open AI API (Open AI, 2024), and LLAMA-2-13B-CHAT (Touvron et al., 2023), and Mask-RCNN (He et al., 2017) for perception, but does not provide specific version numbers for underlying software dependencies like programming languages (e.g., Python), frameworks (e.g., PyTorch/TensorFlow), or other libraries. |
| Experiment Setup | Yes | We set default parameters for LLMs: temperature of 0.7, a maximum of 256 output tokens, and top-1 sampling. |