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# Unity ML-Agents Python Interface and Trainers |
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# Unity ML-Agents Trainers |
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`mlagents` provides a Python API that allows direct interaction with the Unity |
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game engine as well as a collection of trainers and algorithms to train agents |
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in Unity environments. |
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`mlagents` provides a set of reinforcement and imitation learning algorithms designed to be |
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used with Unity environments. The algorithms interface with the Python API provided by the `mlagents_envs` |
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package. See [here](../docs/Python-API.md) for more information on `mlagents_envs`. |
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The `mlagents` Python package contains two sub packages: |
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* `mlagents_envs`: A low level API which allows you to interact directly with a |
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Unity Environment. See |
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[here](../docs/Python-API.md) |
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for more information on using this package. |
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* `mlagents.trainers`: A set of Reinforcement Learning algorithms designed to be |
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used with Unity environments. Access them using the: `mlagents-learn` access |
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point. See |
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[here](../docs/Training-ML-Agents.md) |
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for more information on using this package. |
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The algorithms can be accessed using the: `mlagents-learn` access |
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point. See |
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[here](../docs/Training-ML-Agents.md) |
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for more information on using this package. |
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## Installation |
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For more detailed documentation, check out the |
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[ML-Agents Toolkit documentation.](../docs/Readme.md) |
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## Limitations |
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- `mlagents` does not yet explicitly support multi-agent scenarios so training |
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cooperative behavior among different agents is not stable. |
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- Resuming self-play from a checkpoint resets the reported ELO to the default value. |
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- Resuming curriculum learning from a checkpoint requires the last lesson be specified |
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using the `--lesson` CLI option |
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