/PAE

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Proposer-Agent-Evaluator (PAE): Autonomous Skill Discovery for Foundation Model Internet Agent

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

Yifei Zhou*, Qianlan Yang*, Kaixiang Lin, Min Bai, Xiong Zhou, Yu-Xiong Wang,Sergey Levine, Erran Li
UC Berkeley, UIUC, Amazon
*Equal contribution

method_overview

⚙️ Features

  • State-of-the-art open-source checkpoint for generalist VLM web agent: follow our Quick Start guide to load this checkpoint from huggingface!
  • High performance parallel environment for open-ended VLM web agent: our implementation of web browser environment supports parallel evaluations and data collection for up to 512 concurrent browsers on 8x40 A100 node.
  • Supervised fine-tuning (SFT) pipeline for VLM web agent.
  • Distributed Online RL pipeline for VLM web agent: our online RL pipeline iterates between data collection and policy improvement stages. It also supports distributed data collection across multiple servers.

🚀 Quick Start

Install Python Packages

You need to first create a conda environment and install relevant python packages

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

git clone https://github.com/YifeiZhou02/llava_webagent -b release
cd llava_webagent

# Install PAE
pip install -e .

# Install LLaVA
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
pip install -e .
pip install -e ".[train]"
pip install flash-attn==2.5.9.post1 --no-build-isolation

Install Chrome

You should install google chrome and chrome driver with version 125.0.6422.141 for reproducing our results

sudo apt-get update
wget --no-verbose -O /tmp/chrome.deb https://dl.google.com/linux/chrome/deb/pool/main/g/google-chrome-stable/google-chrome-stable_125.0.6422.141-1_amd64.deb \
  && apt install -y /tmp/chrome.deb \
  && rm /tmp/chrome.deb

wget -O /tmp/chromedriver.zip https://storage.googleapis.com/chrome-for-testing-public/125.0.6422.141/linux64/chromedriver-linux64.zip
cd /tmp
unzip /tmp/chromedriver.zip
mv chromedriver-linux64/chromedriver /usr/local/bin
rm /tmp/chromedriver.zip
rm -r chromedriver-linux64
export PATH=$PATH:/usr/local/bin

Then you can verify that google chrome and chromedriver have been successfully installed with

google-chrome --version
# Google Chrome 125.0.6422.141
chromedriver --version
# ChromeDriver 125.0.6422.141

Play with the Model Yourself

import pae
from pae.models import LlavaAgent, ClaudeAgent
from accelerate import Accelerator
import torch
from tqdm import tqdm
from types import SimpleNamespace
from pae.environment.webgym import BatchedWebEnv
import os
from llava.model.language_model.llava_mistral import LlavaMistralForCausalLM

# ============= Instanstiate the agent =============
config_dict = {"use_lora": False, 
               "use_q4": False, # our 34b model is quantized to 4-bit, set it to True if you are using 34B model
               "use_anyres": False, 
               "temperature": 1.0, 
               "max_new_tokens": 512,
               "train_vision": False,
               "num_beams": 1,}
config = SimpleNamespace(**config_dict)

accelerator = Accelerator() 
agent = LlavaAgent(policy_lm = "yifeizhou/pae-llava-7b", # alternate models "yifeizhou/pae-llava-7b-webarena", "yifeizhou/pae-llava-34b"
                            device = accelerator.device, 
                            accelerator = accelerator,
                            config = config)

# ============= Instanstiate the environment =============
test_tasks = [{"web_name": "Google Map", 
               "id": "0",
          "ques": "Locate a parking lot near the Brooklyn Bridge that open 24 hours. Review the user comments about it.", 
          "web": "https://www.google.com/maps/"}]
save_path = "xxx"

test_env = BatchedWebEnv(tasks = test_tasks,
                        do_eval = False,
                        download_dir=os.path.join(save_path, 'test_driver', 'download'),
                        output_dir=os.path.join(save_path, 'test_driver', 'output'),
                        batch_size=1,
                        max_iter=10,)
# for you to check the images and actions 
image_histories = [] # stores the history of the paths of images
action_histories = [] # stores the history of actions

results = test_env.reset()
image_histories.append(results[0][0]["image"])

observations = [r[0] for r in results]
actions = agent.get_action(observations)
action_histories.append(actions[0])
dones = None

for _ in tqdm(range(3)):
    if dones is not None and all(dones):
        break
    results = test_env.step(actions)
    image_histories.append(results[0][0]["image"])
    observations = [r[0] for r in results]
    actions = agent.get_action(observations)
    action_histories.append(actions[0])
    dones = [r[2] for r in results]

print("Done!")
print("image_histories: ", image_histories)
print("action_histories: ", action_histories)

🖥️ Download Data

You can download all release data from huggingface, including tasks, checkpoints, and example trajectories.

git lfs install
git clone git@hf.co:datasets/yifeizhou/pae-data

You need to process the trajectories to contain its absolute path after downloading it if you wish to perform SFT on it. Change NEW_PATH to the absolute path to the example trajectories in scripts/process_trajectories.py and run:

python scripts/process_trajectories.py

🌎 Set Pre-trained Internet Agent to Wild!

Modify configurations: change save_path, checkpoint_path, and test_tasks in scripts/config/main/evaluate_llava_7b.yaml according to the specifications below. Modify num_processes in scripts/config/accelerate_config to be the number of gpus on your machine. Now you are ready to set your agent to the Internet! The trajectories will be saved in save_path.

cd scripts
TOKENIZERS_PARALLELISM=false accelerate launch --config_file config/accelerate_config/config_zero2.yaml run.py --config-name evaluate_llava_7b_no_eval

Visualize the Trajectories.

We have included a script for you to visualize the trajectories that have been generated. To use it, simply run

cd scripts
python trajectory_visualizer.py {Your previous save_path}

Evaluate the Trajectories and Reproduce Results.

You might have noticed that the evaluation results are all 0. This is because the autonomous evaluator has not been setup. To setup the evaluator, you will need to get an Amazon bedrock account and update the corresponding aws_key_id and aws_secret_key with information in your account. Then you are all set for reproducing our results:

cd scripts
TOKENIZERS_PARALLELISM=false accelerate launch --config_file config/accelerate_config/config_zero2.yaml run.py --config-name evaluate_llava_7b

🧑‍🎓 Supervised Fine-Tuning Example

If you wish to perform supervised fine-tuning, we perform an example workflow. You need to replace the offline_data_path entry in config/main/sft_llava_7b.yaml with the path to which you downloaded the data.

cd scripts
TOKENIZERS_PARALLELISM=false accelerate launch --config_file config/accelerate_config/config_zero2.yaml run.py --config-name sft_llava_7b

On an AWS P4 instance (8xA100 40G), training + evaluation takes ~8hrs.

🏋️ PAE RL Training example

Now you can use the tasks that we have provided to launch an RL run to see how the agents improve on its own! Fill in save_path, checkpoint_path, train_tasks, and test_tasks entries in config/main/rl_llava_7b.yaml in to be the path where you downloaded the data.

cd scripts
TOKENIZERS_PARALLELISM=false accelerate launch --config_file config/accelerate_config/config_zero2.yaml run.py --config-name rl_llava_7b

On an AWS P4 instance (8xA100 40G), each iteration of 1024 trajectories takes around 2 hours, in order to accelerate, consider using parallel data collection servers discussed below. You will need to have evaluator enabled for online RL training, feel free to edit the evaluator to call your favorite model as opposed to default Claude Sonnet 3.

🏟️ WebArena Setup

For WebArena experiments, you need to setup self-hosted WebArena evaluators. We strongly suggest using the pre-installed AWS images from WebArena to set it up. Follow these steps there to set up the server. In addition, since each evaluation run requires resetting the server to a fresh state. We have included resetting remote WebArena servers in our code. To use it,

  • Generate an ssh key and add your public key to ~/.ssh/authorized_keys of the WebArena server
  • Modify to reset.sh and setup.sh be the correct ec2 addresses, and copy them to ~/ of the WebArena.

Then you can test our models in WebArena!

cd scripts
TOKENIZERS_PARALLELISM=false accelerate launch --config_file config/accelerate_config/config_zero2.yaml run.py --config-name evaluate_llava_7b_webarena

📝 PAE Task Proposing Example

If you wish to go through the entire pipeline of PAE including task proposing, you can do so by playing with python scripts/propose_tasks_from_names_webvoyager.py. All hyperparameters can be found at the head of this python file.

🤝 PAE Training with Multiple Data Collection Servers

In order to speed things up, we have provided scripts for accelerating data collection (i.e. trajectory rollouts) since this is the main bottleneck in our online RL training process. You need to follow the following steps to set up remote data collection servers. The remote data collection servers are just for inference and currently we only support trainiing on a single machine. In our main experiments, we use an AWS P4 instance (8x40GB) and 3 AWS G5 instance (8x24GB) for training LLaVa-7B.

  • Make sure both remote data collection servers and the main host machine have access to the same data storage system specified in the save_path argument.
  • Set up the conda environment on remote data collection servers as specified above. Note that we currently only support using the base conda environment on remote data collection servers so you need to install respective packages in the base environment on remote servers.
  • Add the default ssh key of your host machine (usually ~/id_rsa.pub ) to the remote data collection servers (add to ~/.ssh/authorized_keys).

Now you can ready to go! Just keep your remote data collection servers running and no need to run any further commands on those servers manually. Now on your host machine, simply correct the paths in config/main/rl_llava_7b_distributed_server.yaml and run:

cd scripts
TOKENIZERS_PARALLELISM=false accelerate launch --config_file config/accelerate_config/config_zero2.yaml run.py --config-name rl_llava_7b_distributed_server

You should be able to see results logged to your wandb if you have that enabled.

🗂 Specification for Configurations

  • checkpoint_path: The path to the checkpoint that you want the model to start from. If set to null, the pre-trained VLM checkpoint from huggingface will be used.
  • offline_data_path: The path to provided offline data that contains trajectories.pt. Any directory that contains trajectories.pt can be used.
  • save_path: The path to save intermediate checkpoints and interaction trajectories. The script will prioritize loading from save_path for checkpoints and data over checkpoint_path and offline_data_path.
  • policy_lm: The name of the LLaVa model to be used, currently supporting: liuhaotian/llava-v1.6-mistral-7b and liuhaotian/llava-v1.6-34b.
  • use_lora: A boolean flag for whether or not LoRA will be applied for the model. Default: True.
  • train_vision: A boolean flag for whether the parameters for the vision encoder will be updated. Default: False.
  • algorithm: The algorithm to be used for updating the model weights. Supporting: sft and filteredbc.
  • agent_name: The name of the agent. Supporting llava and claude.
  • use_q4: Whether or not to apply 4-bit quantization to the model. Default: False.
  • use_anyres: Whether any-resolution is turned on for LLaVa-1.6. Default: False.
  • capacity: Use the latest N trajectories in the replay buffer for updating the model. Usually a large number will be fine.
  • lm_lr: Learning rate for the model.
  • grad_accum_steps: Gradient Accumulation Steps.
  • online: A boolean flag for whether online data is collected for each gloabl iteration.
  • epochs: Number of global iterations, including a round of data collection if online. You can set it to be a large number to see the entire learning curve on wandb.
  • actor_epochs: Number of epochs to update the policy at each global iteration.
  • actor_trajectories: During each actor_epoch, the number of trajectories to be used for the update. The total number of trajectories sampled (with replacement) for updating the policy is actor_trajectories*actor_epochs.
  • rollout_size: Number of trajectories to be collected at each global iteration.
  • safe_batch_size: The batch size for policy inference during data collection.
  • env_config/batch_size: Number of parallel browsing environment.
  • env_config/max_iter: Maximum number of environment steps for each trajectory. Default: 10.
  • env_config/do_eval: A boolean flag for performing evaluations during rollouts. If set to False, all trajectories will have a default reward of 0.
  • env_config/aws_key_id: AWS bedrock key id for quering Claude for autonomous evaluations. If set to null, the script will try to infer from environment variable.
  • env_config/aws_secret_key: AWS bedrock secret for quering Claude for autonomous evaluations. If set to null, the script will try to infer from environment variable.
  • env_config/webarena_host: When using WebArena, the ec2 address of the webarena server.
  • train_tasks: The .jsonl task file to load the tasks for online training.
  • test_tasks: The .jsonl task file to load the tasks for test evaluations.
  • save_freq: The frequency (in terms of global iterations) for saving model checkpoints. Forced to be 1 while using remote data servers.
  • eval_freq: The frequency to perform evaluations using test tasks.
  • use_wandb: A boolean flag for using Weights and Biases.
  • parallel_options: Supports single, host, and worker. If remote data collection is not used, use single. For remote collection, use host on the host machine. worker`` is used in worker_llava.yaml``` and got automatically passed to the remote servers.
  • worker_ips: A list of IP addresses of your workers (so that the host server can ssh into your remote data collection servers) if remoet data collection is used.
  • host_run_path: The path to this github directory on the host machine if remoet data collection is used.
  • worker_run_path: The path to this github directory on the worker machines if remoet data collection is used.
  • worker_username: The username to ssh into the remote servers from the host machine if remote data collection is used.
  • remote_timeout: The timeout limit for each gloabl iteration on the remote machines in seconds.

Security

See CONTRIBUTING for more information.

License

This dataset is licensed under the CC-BY-NC-4.0 License. See the LICENSE file.