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https://github.com/posgnu/rci-agent

A codebase for "Language Models can Solve Computer Tasks"
https://github.com/posgnu/rci-agent

large-language-models prompting reasoning

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A codebase for "Language Models can Solve Computer Tasks"

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README 

        
# RCI Agent for MiniWoB++

Welcome to the codebase for our paper, "Language Models can Solve Computer Tasks". In this codebase, you will find the implementation of our RCI agent, which uses a pre-trained language model to execute computer tasks in [MiniWoB++ benchmark](http://miniwob.farama.org/) guided by natural language. The agent employs a simple RCI prompting scheme that allows it to improve its outputs.



![overview](./artifacts/overview.gif)



[[Website]](https://posgnu.github.io/rci-web/)

[[Arxiv Paper]](https://arxiv.org/abs/2303.17491v1)

[[PDF]](https://arxiv.org/pdf/2303.17491v1.pdf)



## Dependencies

The RCI agent is implemented in Python 3.9 and requires the following dependencies:



* gym

* openai

* selenium

* Pillow

* regex



```sh

pip install -r requirements.txt

```

Note: [MiniWoB++](https://github.com/stanfordnlp/wge) is not officially supported on Windows. Please refer to [this issue](https://github.com/posgnu/rci-agent/issues/2).



## Usage



### Setup

To run the code, you must first install MiniWoB++ and configure your OpenAI API key. MiniWoB++ is integrated with the OpenAI Gym environment. Navigate to the `computergym` directory and execute the following command to install it:

```sh

cd computergym

pip install -e .

```

Once that's done, you need to write your OpenAI API key in the `example_config.json` file, then rename the file to `config.json`



### Run

To run the code, simply execute the following command:

```sh

python main.py --env [TASK NAME] --llm [LLM NAME] --num-episodes [NUM EPISODES] --erci [NUM Explicit RCI] --irci [NUM Implicit RCI] --sgrounding

```

Here are the arguments you need to specify:

* `--env`: Name of the MiniWoB++ task you want to run. You can see the list of available tasks in `available_tasks.txt`

* `--llm`: Name of the language model you want to use. The model name and corresponding API name are specified below:

    * chatgpt: "gpt-3.5-turbo"

    * davinci: "text-davinci-003"

    * ada: "ada"

    * babbage: "babbage"

    * curie: "curie"

    * davinci1: "davinci"

    * davinci2: "text-davinci-002"

* `--num-episodes`: Number of episodes to run the task

* `--erci`: The number of explicit RCI loop for an action plan. `-1` will remove the action plan sampling.

* `--irci`: The number of implicit RCI loop for the agent grounding.

* `--sgrounding`: If this is True, then the state grounding update is enabled.

* `--headless`: If this is True, then the MiniWoB++ environment will run in headless mode.



Consider running the following command to verify if everything is functioning correctly:

```sh

python main.py --env choose-list --llm chatgpt --num-episodes 1 --irci 1 --sgrounding

```



## Evaluation

Our project's approach has yielded impressive results, with our agent achieving the second-highest score out of all tested models. We have observed that our agent outperforms the baselines, with the exception of CC-Net (SL + RL), which uses dictionary-based typing actions.



![](/artifacts/baseline-1.png)



What sets our RCI agent apart is that it accomplished this feat using 120 times fewer samples than WebN-T5-3B and 11,000 times fewer samples than CC-Net. Obtaining expert demonstrations and defining reward functions for computer tasks can be a daunting challenge, but our research highlights the potential of using LLMs to overcome these obstacles and achieve success in general computer tasks.



![](/artifacts/demos-1.png)



## Check out our paper! 



Our paper is available on [Arxiv](https://arxiv.org/abs/2303.17491v1). If you use this code in your research, we kindly ask that you cite our paper.



```bibtex

@article{kim2023language,

      title={Language Models can Solve Computer Tasks}, 

      author={Geunwoo Kim and Pierre Baldi and Stephen McAleer},

      journal={arXiv preprint arXiv:2303.17491},

      year={2023},

}

```