[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)
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
3 年前