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        "description": "<div align=\"center\"><img src=\"https://raw.githubusercontent.com/chainer/chainerrl/master/assets/ChainerRL.png\" width=\"400\"/></div>\n\n# ChainerRL\n[![Build Status](https://travis-ci.org/chainer/chainerrl.svg?branch=master)](https://travis-ci.org/chainer/chainerrl)\n[![Coverage Status](https://coveralls.io/repos/github/chainer/chainerrl/badge.svg?branch=master)](https://coveralls.io/github/chainer/chainerrl?branch=master)\n[![Documentation Status](https://readthedocs.org/projects/chainerrl/badge/?version=latest)](http://chainerrl.readthedocs.io/en/latest/?badge=latest)\n[![PyPI](https://img.shields.io/pypi/v/chainerrl.svg)](https://pypi.python.org/pypi/chainerrl)\n\nChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using [Chainer](https://github.com/chainer/chainer), a flexible deep learning framework.\n\n![Breakout](assets/breakout.gif)\n![Humanoid](assets/humanoid.gif)\n![Grasping](assets/grasping.gif)\n\n## Installation\n\nChainerRL is tested with Python 2.7+ and 3.5.1+. For other requirements, see [requirements.txt](requirements.txt).\n\nChainerRL can be installed via PyPI:\n```\npip install chainerrl\n```\n\nIt can also be installed from the source code:\n```\npython setup.py install\n```\n\nRefer to [Installation](http://chainerrl.readthedocs.io/en/latest/install.html) for more information on installation. \n\n## Getting started\n\nYou can try [ChainerRL Quickstart Guide](examples/quickstart/quickstart.ipynb) first, or check the [examples](examples) ready for Atari 2600 and Open AI Gym.\n\nFor more information, you can refer to [ChainerRL's documentation](http://chainerrl.readthedocs.io/en/latest/index.html).\n\n## Algorithms\n\n| Algorithm | Discrete Action | Continous Action | Recurrent Model | CPU Async Training |\n|:----------|:---------------:|:----------------:|:---------------:|:------------------:|\n| DQN (including DoubleDQN etc.) | \u2713 | \u2713 (NAF) | \u2713 | x |\n| Categorical DQN | \u2713 | x | \u2713 | x |\n| Rainbow | \u2713 | x | \u2713 | x |\n| IQN | \u2713 | x | x | x |\n| DDPG | x | \u2713 | \u2713 | x |\n| A3C  | \u2713 | \u2713 | \u2713 | \u2713 |\n| ACER | \u2713 | \u2713 | \u2713 | \u2713 |\n| NSQ (N-step Q-learning) | \u2713 | \u2713 (NAF) | \u2713 | \u2713 |\n| PCL (Path Consistency Learning) | \u2713 | \u2713 | \u2713 | \u2713 |\n| PPO  | \u2713 | \u2713 | \u2713 | x |\n| TRPO | \u2713 | \u2713 | x | x |\n| TD3 | x | \u2713 | x | x |\n\nFollowing algorithms have been implemented in ChainerRL:\n- A3C (Asynchronous Advantage Actor-Critic)\n- ACER (Actor-Critic with Experience Replay)\n- Asynchronous N-step Q-learning\n- Rainbow\n- Categorical DQN\n- IQN\n- DQN (including Double DQN, Persistent Advantage Learning (PAL), Double PAL, Dynamic Policy Programming (DPP))\n- DDPG (Deep Deterministic Policy Gradients) (including SVG(0))\n- PGT (Policy Gradient Theorem)\n- PCL (Path Consistency Learning)\n- PPO (Proximal Policy Optimization)\n- TRPO (Trust Region Policy Optimization)\n- TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)\n\nQ-function based algorithms such as DQN can utilize a Normalized Advantage Function (NAF) to tackle continuous-action problems as well as DQN-like discrete output networks.\n\n## Paper Implementations\nThe following papers have been implemented in ChainerRL:\n- [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)\n- [Human-level control through Deep Reinforcement Learning](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)\n- [Deep Reinforcement Learning with Double Q-learning](https://arxiv.org/abs/1509.06461)\n- [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)\n- [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/abs/1511.06581)\n- [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)\n- [A Distributional Perspective on Reinforcement Learning](https://arxiv.org/abs/1707.06887)\n- [Implicit Quantile Networks for Distributional Reinforcement Learning](https://arxiv.org/abs/1806.06923)\n- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://arxiv.org/abs/1710.02298)\n- [Increasing the Action Gap: New Operators for Reinforcement Learning](https://arxiv.org/abs/1512.04860)\n- [Noisy Networks for Exploration](https://arxiv.org/abs/1706.10295)\n- [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)\n- [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347)\n- [Trust Region Policy Optimization](https://arxiv.org/abs/1502.05477)\n- [Sample Efficient Actor-Critic with Experience Replay](https://arxiv.org/abs/1611.01224)\n- [Bridging the Gap Between Value and Policy Based Reinforcement Learning](https://arxiv.org/abs/1702.08892)\n- [Addressing Function Approximation Error in Actor-Critic Methods](https://arxiv.org/abs/1802.09477)\n\n\n## Visualization\n\nChainerRL has a set of accompanying [visualization tools](https://github.com/chainer/chainerrl-visualizer) in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI.\n\n\n## Environments\n\nEnvironments that support the subset of OpenAI Gym's interface (`reset` and `step` methods) can be used.\n\n## Contributing\n\nAny kind of contribution to ChainerRL would be highly appreciated! If you are interested in contributing to ChainerRL, please read [CONTRIBUTING.md](CONTRIBUTING.md).\n\n## License\n\n[MIT License](LICENSE).",
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