gym_tool_use's Introduction

Gym Tool Use

gym tool use environments.

$ pip install gym-tool-use

Usage

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"

Environments

The following environments are registered:

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

Baselines

Baseline implementations here: https://github.com/fomorians/tool-use

Development

Development is started with pipenv.

$ pipenv install
$ pipenv shell

Citation

If you use this code in your work, please cite the following:

@ARTICLE{2019arXiv190702050W,
      author = {{Wenke}, Sam and {Saunders}, Dan and {Qiu}, Mike and {Fleming}, Jim},
       title = "{Reasoning and Generalization in RL: A Tool Use Perspective}",
     journal = {arXiv e-prints},
    keywords = {Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
        year = "2019",
       month = "Jul",
         eid = {arXiv:1907.02050},
       pages = {arXiv:1907.02050},
archivePrefix = {arXiv},
      eprint = {1907.02050},
primaryClass = {cs.NE},
      adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190702050W},
     adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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