https://github.com/xinleipan/gym-gridworld

Simple grid-world environment compatible with OpenAI-gym
https://github.com/xinleipan/gym-gridworld

gridworld openai-gym

Last synced: 4 months ago
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Simple grid-world environment compatible with OpenAI-gym

• Host: GitHub
• URL: https://github.com/xinleipan/gym-gridworld
• Owner: xinleipan
• Created: 2017-06-07T19:58:35.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2020-03-19T22:58:35.000Z (over 6 years ago)
• Last Synced: 2024-11-27T03:34:49.836Z (over 1 year ago)
• Topics: gridworld, openai-gym
• Language: Python
• Homepage:
• Size: 38.1 KB
• Stars: 49
• Watchers: 3
• Forks: 22
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

          
# gym-gridworld

Basic implementation of gridworld game 

for reinforcement learning research. This environment is used in the following paper:

[How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning](https://arxiv.org/abs/1904.11082)

Consider cite the paper:

```

@inproceedings{pan2019you,

  author    = {Xinlei Pan and

               Weiyao Wang and

               Xiaoshuai Zhang and

               Bo Li and

               Jinfeng Yi and

               Dawn Song},

  title     = {How You Act Tells a Lot: Privacy-Leaking Attack on Deep Reinforcement

               Learning},

  booktitle = {Proceedings of the 18th International Conference on Autonomous Agents

               and MultiAgent Systems, {AAMAS}},

  pages     = {368--376},

  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},

  year      = {2019},

}

```

## Install gym-gridworld

install virtual environment for gridworld

    cd gym-gridworld

    conda env create -f environment.yml

    conda activate gridworld

    pip install -e .

## Use gym-gridworld

    

    import gym

    import gym_gridworld

    env = gym.make('gridworld-v0')

    _ = env.reset()

    _ = env.step(env.action_space.sample())

    

## Visualize gym-gridworld

In order to visualize the gridworld, you need to set `env.verbose` to `True`

    env.verbose = True

    _ = env.reset()