An image can be thought of as a matrix of a predefined width (W) and a height (H) and each pixel can be thought of as simply an array of length 3 (in the case of RGB), `[Red, Green, Blue]` holding the different channel information of the color (channel) intensities at that pixel location. Thus an image is just a 3 dimensional matrix of size WxHx3. A Grid Observation can be thought of as a generalization of this setup where in place of a pixel there is a "cell" which is an array of length N representing different channel intensities at that cell position. From a Convolutional Neural Network point of view, the introduction of multiple channels in an "image" isn't a new concept. One such example is using an RGB-Depth image which is used in several robotics applications. The distinction of Grid Observations is what the data within the channels represents. Instead of limiting the channels to color intensities, the channels within a cell of a Grid Observation generalize to any data that can be represented by a single number (float or int).
Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
The Food Collector environment can be described as:
* Set-up: A multi-agent environment where agents compete to collect food.
* C# implementation catered toward a Match-3 setup including concepts around encoding for moves based on [Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/docs/Learning-Environment-Examples.md#match-3).
* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/docs/Learning-Environment-Examples.md#match-3).
### Feedback
If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, [we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the [form](https://forms.gle/TBsB9jc8WshgzViU9). If selected, we will provide gift cards as a token of appreciation.
[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Installation.md#advanced-local-installation-for-development-1)
[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/Installation.md#advanced-local-installation-for-development-1)
See [Git dependencies](https://docs.unity3d.com/Manual/upm-git.html#subfolder) for more information. Note that this
may take several minutes to resolve the packages the first time that you add it.
This version of the Unity ML-Agents Extensions package is compatible with the
following versions of the Unity Editor:
This version of the Unity ML-Agents package is compatible with the following
versions of the Unity Editor:
- If using the `InputActuatorComponent`
- 2019.4 or later
- install the `com.unity.inputsystem` package version `1.1.0-preview.3` or later.
- Else 2018.4 and later
- 2019.4 and later
If using the `InputActuatorComponent`
- install the `com.unity.inputsystem` package version `1.1.0-preview.3` or later.
## Known Limitations
- For the `InputActuatorComponent`
## Need Help?
The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/README.md) contains links for contacting the team or getting support.
The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/README.md) contains links for contacting the team or getting support.
- The minimum supported Unity version was updated to 2019.4. (#5166)
- Several breaking interface changes were made. See the
[Migration Guide](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Migrating.md) for more
details.
- Some methods previously marked as `Obsolete` have been removed. If you were using these methods, you need to replace them with their supported counterpart.
- The interface for disabling discrete actions in `IDiscreteActionMask` has changed.
`WriteMask(int branch, IEnumerable<int> actionIndices)` was replaced with
`SetActionEnabled(int branch, int actionIndex, bool isEnabled)`. (#5060)
- IActuator now implements IHeuristicProvider. (#5110)
- `ISensor.GetObservationShape()` was removed, and `GetObservationSpec()` was added. (#5127)
- The `.onnx` models input names have changed. All input placeholders will now use the prefix `obs_` removing the distinction between visual and vector observations. Models created with this version will not be usable with previous versions of the package (#5080)
- The `.onnx` models discrete action output now contains the discrete actions values and not the logits. Models created with this version will not be usable with previous versions of the package (#5080)
- The `BufferSensor` and `BufferSensorComponent` have been added. They allow the Agent to observe variable number of entities. (#4909)
- The `BufferSensor` and `BufferSensorComponent` have been added. They allow the Agent to observe variable number of entities. For an example, see the [Sorter environment](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Examples.md#sorter). (#4909)
end episodes in groups. (#4923)
end episodes in groups. For examples, see the [Cooperative Push Block](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Examples.md#cooperative-push-block), [Dungeon Escape](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Examples.md#dungeon-escape) and [Soccer](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Examples.md#soccer-twos) environments. (#4923)
/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
Andrew Cohen
4 年前