DigiRL-agent / digirl

Official repo for paper DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning.

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
Oral @ FM Wild, ICML

| Website | Demo | Results | Paper | Checkpoints | Data |

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Research Code for preprint "DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning".

Hao Bai*, Yifei Zhou*, Mert Cemri, Jiayi Pan, Alane Suhr, Sergey Levine, Aviral Kumar
UC Berkeley, UIUC, Google DeepMind
*Equal contribution, alphabetic order; work done at UC Berkeley

🍩 Features

Environment Features

• Auto-adaptive error handling support.
• Multi-machine emulation parallel support.
• Checkpoint resuming support.
• Trajectory video recording support.

Approach Features

• Two training algorithms proposed in the paper

  • DigiRL (automatic curriculum + doubly robust estimator filtering).
  • Filtered Behavior Cloning (reward-based filtering).

• Three training modes:

  • Offline-only training: baseline apporach - use the AutoUI checkpoint to collect data (we have this data ready for you), then train with these pre-collected sub-optimal trajectories. This mode only allows evaluation using the checkpoint.
  • Online-only training: traditional RL approach - the AutoUI checkpoint simultaneously interacts with the environment learns online. This mode allows interactive training.
  • Offline-to-online training: the most powerful approach as evaluated in paper - the AutoUI checkpoint first learns the pre-collected data, then simultanesouly interacts with the environment and do online learning starting from this checkpoint. This mode allows interactive training

• Two agents:

  • AutoUI: we support both training (2 algorithms x 3 paradigms) and evaluation.
  • CogAgent: current only support evaluation, no training pipeline is supported.

• Two Android-in-the-Wild task sets:

  • AitW General: general browsing, opening apps.
  • AitW Web Shopping: shopping on popular shopping websites.
  • It'll also be interesting to explore the other AitW subsets or other task sets if you have good candidates, please propose one in the issue.

• DDP Multi-GPU training:

  • We support accelerate for multi-GPU training. You can turn off this feature if you only have 1 GPU. It only takes 12GB of GPU memory for AutoUI running the DigiRL algorithm, but we provide this feature in case you want to play with something larger.

🚀 Quick Start

Dependencies

First, create a conda environment and install all pip package requirements.

conda create -n digirl python==3.10
conda activate digirl

git clone https://github.com/DigiRL-agent/digirl.git
cd digirl
pip install -e .

Environment Setup

To set up the Android environment for the DigiRL/filtered BC to interact with, refer to the environment README. Before moving on, you should be able to view this screenshot by running this script.

Model checkpoint and Datasets

The SFT checkpoint of the AutoUI model was released here and we use it:

• AutoUI SFT checkpoint

Simply download Auto-UI-Base.zip, then unzip to a directory.

cd <path_to_autoui_dir>
wget https://huggingface.co/cooelf/Auto-UI/resolve/main/Auto-UI-Base.zip
unzip Auto-UI-Base.zip
# wait...
ls Auto-UI-Base
# config.json             pytorch_model.bin        tokenizer.json         training_args.bin
# generation_config.json  special_tokens_map.json  tokenizer_config.json

We provide the pre-collected trajectories using this SFT checkpoint:

• Trajectories of SFT'ed AutoUI

The Google Drive folder contains 4 files, with stats below (you can use gdown to download the checkpoint you want):

File Name	#Trajectories	Horizon	File Size
general-off2on-zeroshot-trajectories.pt	608	10	95.5M
general-offline-zeroshot-trajectories.pt	1552	10	243.9M
webshop-off2on-zeroshot-trajectories.pt	528	20	115.2M
webshop-offline-zeroshot-trajectories.pt	1296	20	297.5M

where general/webshop mean the AitW General/Web Shopping subset, off2on/offline means whether the data is used for offline learning or offline-to-online learning. To make a fair comparison, offline learning should use the similar amount of data that offline-to-online learning finally uses.

Store these files into a directory:

mkdir ~/data && cd ~/data
# copy the .pt file here

If you want to use our final offline-to-online checkpoints to reproduce scores in the paper, you can also download from Google Drive. We release the first offline-to-online checkpoint (run1 in paper) for each algorithm in each environment:

• AutoUI DigiRL & online filtered BC checkpoint

The Google Drive folder also contains 4 files:

File Name	Index in Paper	Test Set Score	File Size
general-off2on-digirl.zip	run1	70.8	1.9G
general-off2on-filteredbc.zip	run1	59.4	1.9G
webshop-off2on-digirl.zip	run1	75.0	1.9G
webshop-off2on-filteredbc.zip	run1	55.2	1.9G

You can also access through Huggingface.

Note that these checkpoints only allows evaluation because we only release the AutoUI checkpoint, not the optimizer states.

Modify Configurations

Then change the huggingface_token, wandb_token, gemini_token, etc. in scripts/config/main/default.yaml, note that you need to specify all entries left blank or <username> for you in this file. This config is the default configuration - you also need to specify the subconfiguration - for example, if you want to run the online algorithm, you should also examine what to modify in scripts/config/main/digirl_online. Feel free to DIY your configs and play with the code!

Run Experiments

After modifying the config to what you like, you can now run experiments with the following commands:

cd scripts
python run.py --config-path config/main --config-name digirl_online

The file run.py is the entrance of the program, and you can pass the config name to run different experiments. The config file is in scripts/config/ directory.

Main Results Reproduction

To reproduce the results in Table 1 of our paper, first download the corresponding checkpoints as described above. As the results in the training set are obtained by randomly sampling tasks, we recommend reproducing the test results (which are obtained by sequentially sampling the first 96 trajectories).

To do this, modify the eval_only.yaml config file and its parent 'default.yaml' config file to experiment settings. For instance, you can modify these configs for reproduction:

1. default.yaml
   1. Set task_split: "test" and eval_sample_mode: "sequential"
   2. Don't forget to increase max_steps to 20 if task_set is set to webshop (as the webshop tasks usually need more steps than the general tasks to complete).
2. eval_only.yaml
   1. Make sure rollout_size (in default.yaml) * eval_iterations (in eval_only.yaml) = 96. For example, rollout_size (16) * eval_iterations (6) = 96.

(Optional) CogAgent server

The way we set CogAgent up is using a Gradio-based API approach, which means that you need to setup CogAgent inference service on a server, then use our code to query that API. To set up CogAgent, refer to the GitHub Page of project AutoEval by Jiayi Pan.

Grab the link and modify that in scripts/config/cogagent/default.yaml file. You need at least one GPU with 48GB memory to host CogAgent for inference.

(Optional) Multi-machine Emulation Parallel

If you want to launch large scale emulation (say more than 32 emulators running at the same time), you'll need multiple machines that collects trajectories at the same time. Refer to the multimachine-training README for details.

(Optional) Multi-GPU DDP Training

We use accelerate for multi-GPU DDP training. To enable, you need to identify the number of GPUs on your machine in the accelerate config. If you model is extremely large, it's also possible to do multi-machine DDP training but we currently don't support it.

To enable this, the only thing you need to do is to replace python run.py with accelerate launch --config_file <config_file> run.py. An example below:

accelerate launch --config_file config/accelerate_config/default_config.yaml run.py --config-path config/main --config-name digirl_off2on

You should be able to see a much faster learning speed if you've successfully set this up.

🌟 Contribution

We welcome the open-source community to contribute to this project. If you invented an algorithm, or you support other types of base models, please propose a PR or issue. Example topics:

• Other algorithms like PPO or any algorithm you invented.
• Other base models like LLaVA.
• Other task sets like WebArena.
• Potential sub-optimal implementations.

📄 License

All content of this work is under Apache License v2.0, including codebase, data, and model checkpoints.

📚 Citation

Consider citing our paper!

@article{bai2024digirl,
  title={DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning},
  author={Bai, Hao and Zhou, Yifei and Cemri, Mert and Pan, Jiayi and Suhr, Alane and Levine, Sergey and Kumar, Aviral},
  journal={arXiv preprint arXiv:2406.11896},
  year={2024}
}