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/blob/release_13_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.
* Goal: The agents must learn to collect as many green food spheres as possible while avoiding red spheres.
* Agents: The environment contains 5 agents with same Behavior Parameters.
When applying the Grid Sensor to this environment, in place of the Raycast Vector Sensor or the Camera Sensor, a Mean Reward of 40-50 is observed. This performance is on par with what is seen by agents trained with RayCasts but the side-by-side comparison of trained agents, shows a qualitative difference in behavior. A deeper study and interpretation of the qualitative differences between agents trained with Raycasts and Vector Sensors verses Grid Sensors is left to future studies.
There are three main phases to the observation process of the Grid Sensor:
### Channel Based
The Channel Based Grid Observations represent obsevations in a normalized form with 0 to 1. To distinguish between categorical and continuous data, one would use the ChannelDepth array to signify the ranges that the values in the `channelValues` array could take. If one sets ChannelDepth[i] to be 1, it is assumed that the value of `channelValues[i]` is already normalized. Else ChannelDepth[i] represents the total number of possible values that `channelValues[i]` can take and will be used for normalization.
The Channel Based Grid Observations is perhaps the simplest in terms of usability and similarity with other machine learning applications. Each grid is of size WxHxC where C is the number of channels. To distinguish between categorical and continuous data, one would use the ChannelDepth array to signify the ranges that the values in the `channelValues` array could take. If one sets ChannelDepth[i] to be 1, it is assumed that the value of `channelValues[i]` is already normalized. Else ChannelDepth[i] represents the total number of possible values that `channelValues[i]` can take.
As the "enemy" is in the second position of the observed tags, its value can be normalized by:
For ObjectType, "weapon", "enemy" will be represented respectively as:
* 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_12_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_13_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_12_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_12_docs/docs/Installation.md#advanced-local-installation-for-development-1)
[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_13_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_13_docs/docs/Installation.md#advanced-local-installation-for-development-1)
- No way to customize the action space of the `InputActuatorComponent`
## Need Help?
The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/README.md) contains links for contacting the team or getting support.
The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/README.md) contains links for contacting the team or getting support.
[Tooltip("The reference of the root of the agent. This is used to disambiguate objects with the same tag as the agent. Defaults to current GameObject")]
publicGameObjectrootReference;
[Header("Collider Buffer Properties")]
[Tooltip("The absolute max size of the Collider buffer used in the non-allocating Physics calls. In other words"+
" the Collider buffer will never grow beyond this number even if there are more Colliders in the Grid Cell.")]
publicintMaxColliderBufferSize=500;
[Tooltip(
"The Estimated Max Number of Colliders to expect per cell. This number is used to "+
"pre-allocate an array of Colliders in order to take advantage of the OverlapBoxNonAlloc "+
"Physics API. If the number of colliders found is >= InitialColliderBufferSize the array "+
"will be resized to double its current size. The hard coded absolute size is 500.")]
publicintInitialColliderBufferSize=4;
Collider[]m_ColliderBuffer;
float[]m_ChannelBuffer;
//
// Hidden Parameters
//
/// <summary>
/// Radius of grid, used for normalizing the distance.
- Added a `--torch-device` commandline option to `mlagents-learn`, which sets the default
[`torch.device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device) used for training. (#4888)
- The `--cpu` commandline option had no effect and was removed. Use `--torch-device=cpu` to force CPU training. (#4888)
- The `mlagents_env` API has changed, `BehaviorSpec` now has a `observation_specs` property containing a list of `ObservationSpec`. For more information on `ObservationSpec` see [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md#behaviorspec). (#4763, #4825)
- The `mlagents_env` API has changed, `BehaviorSpec` now has a `observation_specs` property containing a list of `ObservationSpec`. For more information on `ObservationSpec` see [here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Python-API.md#behaviorspec). (#4763, #4825)
[installation docs](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Installation.md) for
[installation docs](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Installation.md) for
more information on installing PyTorch. For the time being, TensorFlow is still available;
you can use the TensorFlow backend by adding `--tensorflow` to the CLI, or
adding `framework: tensorflow` in the configuration YAML. (#4517)
- The Barracuda dependency was upgraded to 1.1.2 (#4571)
- Utilities were added to `com.unity.ml-agents.extensions` to make it easier to
integrate with match-3 games. See the [readme](https://github.com/Unity-Technologies/ml-agents/blob/master/com.unity.ml-agents.extensions/Documentation~/Match3.md)
integrate with match-3 games. See the [readme](https://github.com/Unity-Technologies/ml-agents/blob/main/com.unity.ml-agents.extensions/Documentation~/Match3.md)
- The Parameter Randomization feature has been refactored to enable sampling of new parameters per episode to improve robustness. The
`resampling-interval` parameter has been removed and the config structure updated. More information [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-ML-Agents.md). (#4065)
`resampling-interval` parameter has been removed and the config structure updated. More information [here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-ML-Agents.md). (#4065)
/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_12_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
В данной статье мы разберем шаг за шагом один из [наших примеров](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Examples.md),
В данной статье мы разберем шаг за шагом один из [наших примеров](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md),
- Для дополнительной информации о ML-Agents Toolkit,
см. [Обзор ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/ML-Agents-Overview.md).
- [Создание своих сцен](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Create-New.md)
см. [Обзор ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/ML-Agents-Overview.md).
- [Создание своих сцен](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Create-New.md)
примера в [ML-Agents - Example Environments](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Examples.md)
- Информация про различные опции обучения - [Training ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-ML-Agents.md)
примера в [ML-Agents - Example Environments](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md)
- Информация про различные опции обучения - [Training ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-ML-Agents.md)
Ervin Teng
4 年前