(PPO) and [Soft Actor-Critic](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Training-SAC.md)
* Built-in support for Imitation Learning
* Flexible agent control with On Demand Decision Making
* Visualizing network outputs within the environment
* Simplified set-up with Docker
## Documentation
## Releases & Documentation
**Our latest, stable release is 0.14.1. Click
[here](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Readme.md) to
get started with the latest release of ML-Agents.**
The table below lists all our releases, including our `master` branch which is under active
development and may be unstable. A few helpful guidelines:
* The docs links in the table below include installation and usage instructions specific to each
release. Remember to always use the documentation that corresponds to the release version you're
using.
* See the [GitHub releases](https://github.com/Unity-Technologies/ml-agents/releases) for more
details of the changes between versions.
* If you have used an earlier version of the ML-Agents Toolkit, we strongly recommend our
[guide on migrating from earlier versions](docs/Migrating.md).
* For more information, in addition to installation and usage instructions, see
the [documentation for the latest release](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Readme.md).
* If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of our [reference paper on Unity and the ML-Agents Toolkit](https://arxiv.org/abs/1809.02627). Also, see below for instructions on citing this paper.
* If you have used an earlier version of the ML-Agents toolkit, we strongly
recommend our [guide on migrating from earlier versions](docs/Migrating.md).
If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print
of our [reference paper on Unity and the ML-Agents Toolkit](https://arxiv.org/abs/1809.02627).
If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following
paper as a reference:
Juliani, A., Berges, V., Vckay, E., Gao, Y., Henry, H., Mattar, M., Lange, D. (2018). Unity: A General Platform for Intelligent Agents. *arXiv preprint arXiv:1809.02627.* https://github.com/Unity-Technologies/ml-agents.
* (February 28, 2020) [Training intelligent adversaries using self-play with ML-Agents](https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/)
* (November 11, 2019) [Training your agents 7 times faster with ML-Agents](https://blogs.unity3d.com/2019/11/11/training-your-agents-7-times-faster-with-ml-agents/)
* (October 21, 2019) [The AI@Unity interns help shape the world](https://blogs.unity3d.com/2019/10/21/the-aiunity-interns-help-shape-the-world/)
* (April 15, 2019) [Unity ML-Agents Toolkit v0.8: Faster training on real games](https://blogs.unity3d.com/2019/04/15/unity-ml-agents-toolkit-v0-8-faster-training-on-real-games/)
* (March 1, 2019) [Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/)
* (December 17, 2018) [ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning](https://blogs.unity3d.com/2018/12/17/ml-agents-toolkit-v0-6-improved-usability-of-brains-and-imitation-learning/)
* (October 2, 2018) [Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit](https://blogs.unity3d.com/2018/10/02/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit/)
* (September 11, 2018) [ML-Agents Toolkit v0.5, new resources for AI researchers available now](https://blogs.unity3d.com/2018/09/11/ml-agents-toolkit-v0-5-new-resources-for-ai-researchers-available-now/)
* (June 26, 2018) [Solving sparse-reward tasks with Curiosity](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/)
* (June 19, 2018) [Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree](https://blogs.unity3d.com/2018/06/19/unity-ml-agents-toolkit-v0-4-and-udacity-deep-reinforcement-learning-nanodegree/)
* (May 24, 2018) [Imitation Learning in Unity: The Workflow](https://blogs.unity3d.com/2018/05/24/imitation-learning-in-unity-the-workflow/)
* (December 11, 2017) [Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
* (December 8, 2017) [Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more](https://blogs.unity3d.com/2017/12/08/introducing-ml-agents-v0-2-curriculum-learning-new-environments-and-more/)
* [Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
See the [GitHub releases](https://github.com/Unity-Technologies/ml-agents/releases) for more details of the changes
between versions.
Please note that the `master` branch is under active development, so the documentation there may differ from the code
of a previous release. Always use the documentation that corresponds to the release version you're using.
## Citation
If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference:
Juliani, A., Berges, V., Vckay, E., Gao, Y., Henry, H., Mattar, M., Lange, D. (2018). Unity: A General Platform for Intelligent Agents. *arXiv preprint arXiv:1809.02627.* https://github.com/Unity-Technologies/ml-agents.
- The method `GetStepCount()` on the Agent class has been replaced with the property getter `StepCount`
- `RayPerceptionSensorComponent` and related classes now display the debug gizmos whenever the Agent is selected (not just Play mode).
- Most fields on `RayPerceptionSensorComponent` can now be changed while the editor is in Play mode. The exceptions to this are fields that affect the number of observations.
- Most fields on `CameraSensorComponent` and `RenderTextureSensorComponent` were changed to private and replaced by properties with the same name.
- The `IFloatProperties` interface has been removed.
* `BrainParameters` and `SpaceType` have been removed from the public API
* `BehaviorParameters` have been removed from the public API.
* `DecisionRequester` has been made internal (you can still use the DecisionRequesterComponent from the inspector). `RepeatAction` was renamed `TakeActionsBetweenDecisions` for clarity.
* The `IFloatProperties` interface has been removed.
### Steps to Migrate
* Add the `using MLAgents.Sensors;` in addition to `using MLAgents;` on top of your Agent's script.
iterate through `RayPerceptionOutput.rayOutputs` and call `RayPerceptionOutput.RayOutput.ToFloatArray()`.
* Re-import all of your `*.NN` files to work with the updated Barracuda package.
* Replace all calls to `Agent.GetStepCount()` with `Agent.StepCount`
* Replace all calls to `Agent.GetStepCount()` with `Agent.StepCount`.
* Replace `IFloatProperties` variables with `FloatPropertiesChannel` variables.
GitHub
5 年前
