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45 lines
1.9 KiB
Markdown
45 lines
1.9 KiB
Markdown
# Coach Features
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## Supported Algorithms
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Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into two main classes -
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value optimization and policy optimization. A detailed description of those algorithms may be found in the algorithms
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section.
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<p style="text-align: center;">
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<img src="../../img/algorithms.png" alt="Supported Algorithms" style="width: 600px;"/>
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</p>
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## Supported Environments
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Coach supports a large number of environments which can be solved using reinforcement learning:
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* **[DeepMind Control Suite](https://github.com/deepmind/dm_control)** - a set of reinforcement learning environments
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powered by the MuJoCo physics engine.
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* **[Blizzard Starcraft II](https://github.com/deepmind/pysc2)** - a popular strategy game which was wrapped with a
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python interface by DeepMind.
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* **[ViZDoom](http://vizdoom.cs.put.edu.pl/)** - a Doom-based AI research platform for reinforcement learning
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from raw visual information.
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* **[CARLA](https://github.com/carla-simulator/carla)** - an open-source simulator for autonomous driving research.
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* **[OpenAI Gym](https://gym.openai.com/)** - a library which consists of a set of environments, from games to robotics.
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Additionally, it can be extended using the API defined by the authors.
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In Coach, we support all the native environments in Gym, along with several extensions such as:
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* **[Roboschool](https://github.com/openai/roboschool)** - a set of environments powered by the PyBullet engine,
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that offer a free alternative to MuJoCo.
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* **[Gym Extensions](https://github.com/Breakend/gym-extensions)** - a set of environments that extends Gym for
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auxiliary tasks (multitask learning, transfer learning, inverse reinforcement learning, etc.)
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* **[PyBullet](https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet)** - a physics engine that
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includes a set of robotics environments.
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