Learning to Model the World with Language

This repository contains the code for the paper Learning to Model the World with Language. We introduce Dynalang, an agent that leverages diverse types of language to solve tasks by using language to predict the future via a multimodal world model.

For more information, check out:

• Project Site
• Paper

Getting Started

Install dependencies:

pip install -e .

🏠 HomeGrid

Install the HomeGrid environment and run the example training script with the task name (homegrid_task, homegrid_future, homegrid_dynamics, or homegrid_corrections):

pip install homegrid
sh scripts/run_homegrid.sh homegrid_task EXP_NAME GPU_IDS SEED

👾 Messenger

1. Install dependencies for the Messenger env:

sudo apt-get install \
  libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \
  libsdl1.2-dev libsmpeg-dev subversion libportmidi-dev ffmpeg \
  libswscale-dev libavformat-dev libavcodec-dev libfreetype6-dev

2. Clone the Messenger environment and install it:

git clone https://github.com/ahjwang/messenger-emma 
pip install -e messenger-emma

3. Download the pre-embedded sentences from Google Drive and place the pkl file into dynalang/embodied/envs/data.

4. Run the example training script for s1, s2, or s3:

sh scripts/run_messenger_s1.sh EXP_NAME GPU_IDS SEED

🛋 VLN

1. Install dependencies for VLN. Since VLN-CE depends on an older version of the Habitat simulator, we provide a conda yml file with pinned dependency versions and recommend you install the same version of JAX.

conda create -n dynalang-vln python=3.8
pip install "jax[cuda11_cudnn82]==0.4.8" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install torch torchvision
conda env update -f env_vln.yml
conda install -c aihabitat -c conda-forge habitat-sim=0.1.7 headless

2. Clone our VLN-CE and habitat-lab forks into this project repo:

git clone https://github.com/jlin816/VLN-CE VLN_CE
git clone https://github.com/jlin816/habitat-lab habitat_lab

3. Download Matterport3D data into the VLN-CE directory (requires Python 2.7): additional instructions

conda create -n py27 python=2.7
conda activate py27
python scripts/download_mp.py --task habitat -o VLN_CE/data/scene_datasets/mp3d/
cd VLN_CE/data/scene_datasets
unzip mp3d/v1/tasks/mp3d_habitat.zip
conda deactivate

Scenes should now be populated into directories under VLN_CE/data/scene_datasets/mp3d/{scene_dir}/....

4. Download Room-to-Room dataset into the VLN_CE directory: additional instructions

mkdir -p VLN_CE/data/datasets
cd VLN_CE/data/datasets
gdown https://drive.google.com/uc?id=1kQ_at68wiK2vAmlWbjJ4EDrLtaM0nfkR
unzip R2R_VLNCE_v1-3_preprocessed.zip

5. Download the pre-embedded sentences from Google Drive and place the pkl file into dynalang/embodied/envs/data.

6. Run the example training script:

sh scripts/run_vln.sh EXP_NAME GPU_IDS SEED

💬 LangRoom

LangRoom must be run from the langroom branch, which contains changes to support LangRoom's dictionary action space for moving and talking simultaneously. Install the LangRoom environment and run the example training script:

git checkout langroom
pip install langroom
sh run_langroom.sh EXP_NAME GPU_IDS SEED

📑 Text Pretraining and Finetuning

Install additional dependencies and run the example training script to train on TinyStories. By default, the script initializes a model with the observation and action space of the Messenger S2 task and monitors loss on a replay buffer while training. You'll need to pass a path to the .../episodes/ directory (containing the .npz chunks) of a previous Messenger S2 agent if you want to do the same.

pip install datasets
sh scripts/pretrain_text.sh EXP_NAME GPU_IDS SEED roneneldan/TinyStories /PATH/TO/EVAL/REPLAY/EPISODES

To load a pretrained world model into an agent for finetuning, run the train script with the following flags (load_wm_ckpt only loads the weights of the world model and initializes the rest of the agent from scratch):

python dynalang/train.py \
  --load_wm_ckpt True \
  --run.from_checkpoint /path/to/pretraining/checkpoint.pkl \
  ...

Training Configuration Tips

• Note that to use the same batch sizes as we did in the paper, you may have to use more than one GPU. To do so, run the script with a list of GPU IDs and additionally specify which GPUs should be used for training the world model and running the policy (e.g. sh run_messenger_s3.sh EXP_NAME 0,1,2,3 SEED --jax.train_devices 0,1,2,3 --jax.policy_devices 0.
• You may want to use the parallel script for training speedups. See scripts/run_vln.sh for an example.

Acknowledgments

This repo is adapted from DreamerV3.

Citation

@article{lin2023learning,
         title={Learning to Model the World with Language},
         author={Jessy Lin and Yuqing Du and Olivia Watkins and Danijar Hafner and Pieter Abbeel and Dan Klein and Anca Dragan},
         year={2023},
         eprint={2308.01399},
         archivePrefix={arXiv},
}