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    "info": {
        "author": "M\u00e1rcio Porto",
        "author_email": "mflporto@gmail.com",
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        "description": "# rlib\n\n[![Build Status](https://travis-ci.org/MarcioPorto/rlib.svg?branch=master)](https://travis-ci.org/MarcioPorto/rlib)\n\n`rlib` is a small deep reinforcement learning library with implementations of popular deep RL algorithms. Each algorithm is highly modular and customizable, making this library a great choice for anyone who wants to test the performance of different algorithms in the same environment. `rlib` uses PyTorch as the library of choice for its initial version, but support for TensorFlow is on the roadmap.\n\n## Installation\n\n```\npip install rlib\n```\n\n## Usage\n\nUsing `rlib` is this simple:\n\n```python\nfrom rlib.algorithms.dqn import DQNAgent\nfrom rlib.environments.gym import GymEnvironment\n\n\ne = gym.make('CartPole-v0')\n\nobservation_size = 4\naction_size = 2\n\ndqn = DQNAgent(observation_size, action_size)\nenv = GymEnvironment(e, dqn)\nenv.train()\nenv.test()\n```\n\n## Advanced\n\n### TensorBoard and GIFRecorder\n\n1. Initialize `Logger` and/or `GIFRecorder` objects. \n\n```python\nos.makedirs('your/log/dir', exist_ok=True)\n\nlogger = Logger(output_dir)\ngifs_recorder = GIFRecorder(output_dir, duration=3.0)\n```\n\n2. Initialize a new environment using these objects.\n\n```python\nenv = GymEnvironment(e, dqn, logger=logger, gifs_recorder=gifs_recorder)\n```\n\n3. To check Tensorboard logs, run:\n\n```\ntensorboard --logdir=your/log/dir\n```\n\n### Custom models\n\n1. Define your own custom model.\n\n```python\nclass NeuralNet(torch.nn.Module):\n    def __init__(self):\n        super(NeuralNet, self).__init__()\n        self.fc1 = nn.Linear(4, 8) \n        self.relu = nn.ReLU()\n        self.fc2 = nn.Linear(8, 2)  \n\n    def forward(self, x):\n        out = self.fc1(x)\n        out = self.relu(out)\n        out = self.fc2(out)\n        return out\n```\n\n2. Check the documentation for the algorithm you are using for the appropriate argument name. For DQN:\n\n```python\ndqn = DQNAgent(\n    observation_size, action_size,\n    qnetwork_local=NeuralNet(),\n    qnetwork_target=NeuralNet(),\n)\n```\n\n### Saving model weights\n\n1. Set the `model_output_dir` argument when creating a new instance of an algorithm to the directory where you want your model to be saved.\n\n## Testing\n\nTo run all tests:\n\n```\npython -m unittest discover test/\n```\n\n## Contributing\n\n### Installation\n\nFeel free to open issues with any bugs found or any feature requests. Pull requests are always welcome for new functionality.\n\n```\nvirtualenv -p python3 venv\ncd rlib/\nsource venv/bin/activate\npip install -r requirements.txt\n```\n\nTo make sure your installation worked, run one of the algorithms in the test folder:\n\n```\npython test/algorithms/dqn_test.py\n```\n\n## License\n\n`rlib` is released under the [MIT License](https://github.com/MarcioPorto/rlib/blob/master/LICENSE.md).\n\n\n\n",
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        "summary": "Implementations of popular Deep Reinforcement Learning algorithms",
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