Awesome
Multi-Agent-Framework (Website, ICRA 2024)
Here we show the related code for the Multi-Agent Framework paper. The code will be updated dynamically in the future. There are in total four environments, corresponding to BoxNet1, BoxNet2, BoxLift, and Warehouse, respectively.
<div align="center"> <img src="Github-figures/main_figure.png" alt="Main image" width="75%"/> </div>Requirements
Please install the following Python packages.
pip install numpy openai re random time copy tiktoken
Then you need to get your OpenAI key from https://beta.openai.com/ Put that OpenAI key starting 'sk-' into the LLM.py, line8
Create testing trial environments
Run the env1_create.py/env2_create.py/env3_create.py/env4_create.py to create the environments, remember change the Code_dir_path in the last lines.
python env1_create.py
Usage
Run the env1-box-arrange.py/env2-box-arrange.py/env3-box-arrange.py/env4-box-arrange.py to test our approaches in different frameworks and dialogue history methods. In around Line270, set up the models(GPT-3/4), frameworks (HMAS-2,HMSA-1, DMAS,CMAS), dialogue history method, and your working path dir. Then run the script:
python env1-box-arrange.py
The experimental results will appear in the generated dir Env1_BoxNet1. For visualizing the testing results, set up the Code_dir_path in line2, then run the script:
python data_visua.py
Recommended Work
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models