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.
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
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
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
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models