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        "author": "Phil Winder",
        "author_email": "phil@winderresearch.com",
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        "description": "# gym-display-advertising\n\nAn OpenAI Gym for Display Advertisment Reinforcement Learning\n\n## Installation\n\n`pip install gym-display-advertising`\n\n## Usage\n\n```python\nimport gym_display_advertising\n\nenv = gym.make(\"StaticDisplayAdvertising-v0\")\nepisode_over = False\nwhile not episode_over:\n    state, reward, episode_over, _ = env.step(env.action_space.sample())\n    print(state, reward)\n```\n\n```python\nimport gym_display_advertising\n\nenv = gym.make(\"DisplayAdvertising-v0\")\nepisode_over = False\nwhile not episode_over:\n    state, reward, episode_over, _ = env.step(env.action_space.sample())\n    print(state, reward)\n```\n\n## Real Ad Bidding Data\n\nThe repository contains real-life bidding data from a single merchant and loads this by default. If you want to load more data follow the instructions in the [make-ipinyou-data repository](https://github.com/wnzhang/make-ipinyou-data) to create the data.\n\nThen use the helper class `ProcessedIPinYouData` to load the data and pass the dataframe into the `gym.make` command.\n\n```python\nimport pathlib\nimport gym\nimport gym_display_advertising\n\nipinyou = gym_display_advertising.data.ProcessedIPinYouData(directory=pathlib.Path(\"path/to/file\"))\ntraining_data, _ = ipinyou.get_merchant_data(2997)\nenv = gym.make(\"DisplayAdvertising-v0\", data=training_data)\nstate, reward, _, _ = env.step(env.action_space.sample())\nprint(state, reward)\n```\n\n## Acknowledgements\n\nThis is a project by [Winder Research](https://WinderResearch.com), a Cloud-Native Data Science consultancy.\n\n\n",
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