BrowserGym: a gym environment for web task automation in the Chromium browser
Project description
BrowserGym
This package provides browsergym, a gym environment for web task automation in the Chromium browser.
Setup
- Install the package
pip install browsergym
- Install playwright browsers
playwright install
- For each benchmark, specific setup instructions must be followed
- for miniwob, see miniwob/README.md
- for webarena, see webarena/README.md
- for workarena, see the WorkArena repository
Usage
Open-ended task example
Boilerplate code to run an agent on the openended task:
import gymnasium as gym
import browsergym.core # register openended taskj as a gym environment
env = gym.make(
"browsergym/openended", start_url="https://www.google.com/", wait_for_user_message=True
)
obs, info = env.reset()
done = False
while not done:
action = ... # implement your agent here
obs, reward, terminated, truncated, info = env.step(action)
MiniWoB++ task example
Boilerplate code to run an agent on a miniwob task:
import gymnasium as gym
import browsergym.miniwob # register miniwob tasks as gym environments
env = gym.make("browsergym/miniwob.choose-list")
obs, info = env.reset()
done = False
while not done:
action = ... # implement your agent here
obs, reward, terminated, truncated, info = env.step(action)
WebArena task example
Boilerplate code to run an agent on a webarena task:
import gymnasium as gym
import browsergym.webarena # register webarena tasks as gym environments
env = gym.make("browsergym/webarena.310")
obs, info = env.reset()
done = False
while not done:
action = ... # implement your agent here
obs, reward, terminated, truncated, info = env.step(action)
WorkArena task example
Boilerplate code to run an agent on a workarena task:
import gymnasium as gym
import browsergym.workarena # register workarena tasks as gym environments
env = gym.make("browsergym/workarena.servicenow.order-ipad-pro")
obs, info = env.reset()
done = False
while not done:
action = ... # implement your agent here
obs, reward, terminated, truncated, info = env.step(action)
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