Skip to main content

Gym tool use environments.

Project description

Gym Tool Use

gym tool use environments.

$ pip install gym-tool-use


import gym_tool_use  # import to register gym envs
env = gym.make("TrapTube-v0")
observation = env.reset()
action = env.action_space.sample()
observation_next, reward, done, info = env.step(action)
image = env.render(mode="rgb_array")  # also supports mode="human"


The following environments are registered:

  • "TrapTube-v0" (base task)
  • "PerceptualTrapTube-v0"
  • "StructuralTrapTube-v0"
  • "SymbolicTrapTube-v0"
  • "PerceptualSymbolicTrapTube-v0"
  • "StructuralSymbolicTrapTube-v0"
  • "PerceptualStructuralTrapTube-v0"
  • "PerceptualStructuralSymbolicTrapTube-v0"


Baseline implementations here:


Development is started with pipenv.

$ pipenv install
$ pipenv shell

Project details

Release history Release notifications

This version


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for gym-tool-use, version 1.0.0
Filename, size File type Python version Upload date Hashes
Filename, size gym-tool-use-1.0.0.tar.gz (12.9 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page