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multi-agent-framework

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LLM multi-agent discussion framework for multi-agent/robot situations.

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# Multi-Agent-Framework ([Website](https://yongchao98.github.io/MIT-REALM-Multi-Robot/), 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.

## 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](https://arxiv.org/pdf/2306.06531.pdf)

[NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models](https://arxiv.org/pdf/2305.07766.pdf)

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