--- title: 'GenRL: Multimodal foundation world models for generalist embodied agents' python_version: 3.1 sdk: gradio sdk_version: 4.36.1 app_file: app.py pinned: false license: mit colorFrom: green colorTo: green --- # GenRL: Multimodal foundation world models for generalist embodied agents

Website  | Models 🤗  | Datasets 🤗  | Gradio demo  | Notebooks 
## Get started ### Creating the environment We recommend using `conda` to create the environment ``` conda create --name genrl python=3.10 conda activate genrl pip install -r requirements.txt ``` ### Downloading InternVideo2 Download InternVideo 2 [[here]](https://huggingface.co./OpenGVLab/InternVideo2-Stage2_1B-224p-f4/blob/main/InternVideo2-stage2_1b-224p-f4.pt). Place in the `models` folder. Note: the file access is restricted, so you'll need an HuggingFace account to request access to the file. Note: By default, the code expects the model to be placed in the `models` folder. The variable `MODELS_ROOT_PATH` indicating where the model should be place is set in `tools/genrl_utils.py`. ## Data ### Download datasets The datasets used to pre-trained the models can be downloaded [[here]](https://huggingface.co./datasets/mazpie/genrl_datasets). The file are `tar.gz` and can be extracted using the `tar` utility on Linux. For example: ``` tar -zxvf walker_data.tar.gz ``` ### Collecting and pre-processing data If you don't want to download our datasets, you collect and pre-process the data on your own. Data can be collected running a DreamerV3 agent on a task, by running: ``` python3 collect_data.py agent=dreamer task=stickman_walk ``` or the Plan2Explore agent, by running: ``` python3 collect_data.py agent=plan2explore conf/defaults=dreamer_v2 task=stickman_walk ``` A repo for the experiment will be created under the directory `exp_local`, such as: `exp_local/YYYY.MM.DD/HHMMSS_agentname`. The data can then be found in the `buffer` subdirectory. After obtaining the data, it should be processed to obtain the video embeddings for each frame sequence in the episodes. The processing can be done by running: ``` python3 process_dataset.py dataset_dir=data/stickman_example ``` where `data/stickman_example` is replaced by the folder of the data you want to process. ## Agents ### Downloading pre-trained models If you want to test our work, without having to pre-train the models, you can do this by using our pre-trained models. Pretrained models can be found [[here]](https://huggingface.co./mazpie/genrl_models) Here's a snippet to download them easily: ``` import os from huggingface_hub import hf_hub_download def download_model(model_folder, model_filename): REPO_ID = 'mazpie/genrl_models' filename_list = [model_filename] if not os.path.exists(model_folder): os.makedirs(model_folder) for filename in filename_list: local_file = os.path.join(model_folder, filename) if not os.path.exists(local_file): hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir=model_folder, local_dir_use_symlinks=False) download_model('models', 'genrl_stickman_500k_2.pt') ``` Pre-trained models can be used by setting `snapshot_load_dir=...` when running `train.py`. Note: the pre-trained models are not trained to solve any tasks. They only contain a pre-trained multimodal foundation world model (world model + connector and aligner). ### Training multimodal foundation world models In order to train a multimodal foundation world model from data, you should run something like: ``` # Note: frames = update steps python3 train.py task=stickman_walk replay_load_dir=data/stickman_example num_train_frames=500_010 visual_every_frames=25_000 train_world_model=True train_connector=True reset_world_model=True reset_connector=True ``` ### Behavior learning After pre-training a model, you can train the behavior for a task using: ``` python3 train.py task=stickman_walk snapshot_load_dir=models/genrl_stickman_500k_2.pt num_train_frames=50_010 batch_size=32 batch_length=32 agent.imag_reward_fn=video_text_reward eval_modality=task_imag ``` Data-free RL can be performed by additionaly passing the option: `train_from_data=False` The prompts for each task can be found and edited in `tools/genrl_utils.py`. However, you can also pass a custom prompt for a task by passing the option: `+agent.imag_reward_args.task_prompt=custom_prompt` ## Other utilities ### Gradio demo There's a gradio demo that can be found at `demo/app.py`. If launching demo like a standard Python program with: ``` python3 demo/app.py ``` it will return a local endpoint (e.g. http://127.0.0.1:7860) where to access a dashboard to play with GenRL.

### Notebooks You can find several notebooks to test our code in the `notebooks` directory. `demo_videoclip` : can be used to test the correct functioning of the InternVideo2 component `text2video` : utility to generate video reconstructions from text prompts `video2video` : utility to generate video reconstructions from video prompts `visualize_dataset_episodes` : utility to generate videos from the episodes in a given dataset `visualize_env` : used to play with the environment and, for instance, understand how the reward function of each task works ### Stickman environment We introduced the Stickman environment as a simplified 2D version of the Humanoid environment. This can be found in the `envs/custom_dmc_tasks` folder. You will find an `.xml` model and a `.py` files containing the tasks. ## Acknowledgments We would like to thank the authors of the following repositories for their useful code and models: * [InternVideo2](https://github.com/OpenGVLab/InternVideo) * [Franka Kitchen](https://github.com/google-research/relay-policy-learning) * [DreamerV3](https://github.com/danijar/dreamerv3) * [DreamerV3-torch](https://github.com/NM512/dreamerv3-torch)