https://github.com/1989Ryan/llm-mcts

[NeurIPS 2023] We use large language models as commonsense world model and heuristic policy within Monte-Carlo Tree Search, enabling better-reasoned decision-making for daily task planning problems.
https://github.com/1989Ryan/llm-mcts

large-language-models neurips-2023 task-planning

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[NeurIPS 2023] We use large language models as commonsense world model and heuristic policy within Monte-Carlo Tree Search, enabling better-reasoned decision-making for daily task planning problems.

• Host: GitHub
• URL: https://github.com/1989Ryan/llm-mcts
• Owner: 1989Ryan
• License: apache-2.0
• Created: 2023-05-22T02:10:03.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-25T11:36:29.000Z (7 months ago)
• Last Synced: 2024-04-06T17:36:29.580Z (6 months ago)
• Topics: large-language-models, neurips-2023, task-planning
• Language: Python
• Homepage: https://llm-mcts.github.io/
• Size: 746 KB
• Stars: 64
• Watchers: 4
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# llm-mcts

This repository contains the code for NeurIPS'23 paper: [Large language models as commonsense knowledge for large-scale task planning](https://arxiv.org/abs/2305.14078). 

We use Large Language Models as both the commonsense world model and the heuristic policy within Monte Carlo Tree Search. LLM's world model provides with MCTS a commonsense prior belief of states for reasoned decision-making. The LLM's heuristic policy guides the search to relevant parts of the tree, substantially reducing the search complexity.

![Figure](media/llm-mcts.jpeg)

## Updates

* [25 Feb 2024] We have updated the code to use the latest version of the OpenAI API. 

## Cite

```

@inproceedings{

  zhao2023large,

  title={Large Language Models as Commonsense Knowledge for Large-Scale Task Planning},

  author={Zirui Zhao and Wee Sun Lee and David Hsu},

  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},

  year={2023},

  url={https://openreview.net/forum?id=Wjp1AYB8lH}

}

```

## Install

Install the repo: 

```

git clone --recurse-submodules https://github.com/1989Ryan/llm-mcts.git

```

You need to first install virtual home. Please follow with the link at [here](./vh/vh_sim/README_Download.md) as well as the official repository at [here](https://github.com/xavierpuigf/virtualhome) to install.

To intall the dependencies in our method, run

```

pip install -r requirement.txt

```

## Generate Data

We use the code from [here](https://github.com/xavierpuigf/watch_and_help) to generate the data. You can also use the script at [here](./scripts/gene_data.sh) to generate the data. 

To generate data, you need to generate the goal of a domain first, using the command

```

python vh/data_gene/gen_data/vh_init.py \

    --port "{Port Number}" \

    --task {choose your task} \

    --mode {choose one difficulty} \

    --usage {training or testing} \

    --num-per-apartment {a number} 

```

Then, to generate expert data, you need to use

```

python vh/data_gene/testing_agents/gene_data.py \

    --mode {difficulty} \

    --dataset_path {the path to the file generated in the previous step}\

    --base-port {port number}

```

After that, we need to pre-process the expert data

```

python mcts/virtualhome/expert_data.py

```

## Run

Add your openai api key in both `./mcts/virtualhome/llm_model.py` and `./mcts/virtualhome/llm_policy.py`. 

Generate the world model by LLM:

```

python mcts/virtualhome/llm_model.py

```

To run the code for LLM-MCTS, use

```

python mcts/virtualhome/mcts_agent.py \

    --exploration_constant 24 \

    --max_episode_len 50 \

    --max_depth 20 \

    --round 0 \

    --simulation_per_act 2 \

    --simulation_num 100 \

    --discount_factor 0.95  \

    --uct_type PUCT \

    --mode simple \

    --seen_item \

    --seen_apartment\

    --model gpt-3.5-turbo-0125 \

    --seen_comp

```

## Acknowledge

This repository is built upon a number of prior opensource works. 

* Our data generation and testing settings are adapted from https://github.com/xavierpuigf/watch_and_help. 

* The baseline (fine-tuned GPT2 policy) is adapted from https://github.com/ShuangLI59/Pre-Trained-Language-Models-for-Interactive-Decision-Making. Their training code is available in the supplementary materials at https://openreview.net/forum?id=FWMQYjFso-a. 

* GPT3.5 baseline is adapted from https://github.com/huangwl18/language-planner. 

* Our MCTS implementation is adapted from https://github.com/jys5609/MC-LAVE-RL.