{ "info": { "author": "Vivek Verma", "author_email": "vivnps.verma@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "# ATC AI\nContains\n* A program that helps minimize air crashes by \nusing Machine Learning to control air traffic.\n* A gym environment for ATC simulation\n## Implementing\n* `env.render()` is not implemented, running it will raise `NotImplementedError`.\n* `env.reset()` opens the GUI.\n* `env.fps` contains the fps to run the game at. You can set it using:\n ```python\n env.fps = 60\n ```\n## Installation\nFor the latest installation (may be unstable) \n```bash\ngit clone https://github.com/vivek3141/atc-ai\npip install -e .\n```\nInstall stable release by\n```bash\npip install atc-gym\n```\n\n## Creating The Environment\nThe environment can be created by doing the following:\n```python\nimport gym\nimport atc_gym\nenv = gym.make(\"atc-v0\")\n```\n\n## Environments\n* `atc-v0` Returns a NxN RGB image in the form of a numpy array for the observations\n* `atc-tiled-v0` Returns a NxN matrix for the observations.\n
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