Updated references to Unity Inference Engine to Sentis. (Unity-Technologies#5998)

Author: miguelalonsojr

docs/Getting-Started.md

@@ -92,7 +92,7 @@ itself to keep the ball balanced on its head.
 ## Running a pre-trained model
 
 We include pre-trained models for our agents (`.onnx` files) and we use the
-[Unity Inference Engine](Unity-Inference-Engine.md) to run these models inside
+[Sentis](Sentis.md) to run these models inside
 Unity. In this section, we will use the pre-trained model for the 3D Ball
 example.
 

docs/ML-Agents-Overview.md

@@ -277,9 +277,9 @@ mode with the **3D Balance Ball** sample environment.
 #### Cross-Platform Inference
 
 It is important to note that the ML-Agents Toolkit leverages the
-[Unity Inference Engine](Unity-Inference-Engine.md) to run the models within a
+[Sentis](Sentis.md) to run the models within a
 Unity scene such that an agent can take the _optimal_ action at each step. Given
-that the Unity Inference Engine support most platforms that Unity does, this
+that Sentis support most platforms that Unity does, this
 means that any model you train with the ML-Agents Toolkit can be embedded into
 your Unity application that runs on any platform. See our
 [dedicated blog post](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/)

docs/ML-Agents-Toolkit-Documentation.md

@@ -28,7 +28,7 @@
   - [Training Configuration File](Training-Configuration-File.md)
   - [Using TensorBoard to Observe Training](Using-Tensorboard.md)
   - [Profiling Trainers](Profiling-Python.md)
-- [Unity Inference Engine](Unity-Inference-Engine.md)
+- [Sentis](Sentis.md)
 
 ## Extending ML-Agents
 

docs/Migrating.md

@@ -155,7 +155,7 @@ Sensors with non-normalized data cannot use PNG compression type.
 recieved from `GetObjectData()` will be the observation sent to the trainer.
 
 ### LSTM models from previous releases no longer supported
-The way the Unity Inference Engine processes LSTM (recurrent neural networks) has changed. As a result, models
+The way that Sentis processes LSTM (recurrent neural networks) has changed. As a result, models
 trained with previous versions of ML-Agents will not be usable at inference if they were trained with a `memory`
 setting in the `.yaml` config file.
 If you want to use a model that has a recurrent neural network in this release of ML-Agents, you need to train
@@ -705,7 +705,7 @@ vector observations to be used simultaneously.
 ### Important Changes
 
 - We no longer support TFS and are now using the
-  [Unity Inference Engine](Unity-Inference-Engine.md)
+  [Sentis](Sentis.md)
 
 #### Steps to Migrate
 

docs/Readme.md

@@ -34,7 +34,7 @@ developer communities.
 - Train robust agents using environment randomization
 - Flexible agent control with On Demand Decision Making
 - Train using multiple concurrent Unity environment instances
-- Utilizes the [Unity Inference Engine](Unity-Inference-Engine.md) to
+- Utilizes the [Sentis](Sentis.md) to
   provide native cross-platform support
 - Unity environment [control from Python](Python-LLAPI.md)
 - Wrap Unity learning environments as a [gym](Python-Gym-API.md) environment

docs/Unity-Inference-Engine.md docs/Sentis.md

@@ -1,25 +1,25 @@
-# Unity Inference Engine
+# Sentis
 
 The ML-Agents Toolkit allows you to use pre-trained neural network models inside
 your Unity games. This support is possible thanks to the
-[Unity Inference Engine](https://docs.unity3d.com/Packages/com.unity.sentis@latest/index.html)
-(codenamed Sentis). The Unity Inference Engine uses
+[Sentis](https://docs.unity3d.com/Packages/com.unity.sentis@latest/index.html)
+(codenamed Sentis). Sentis uses
 [compute shaders](https://docs.unity3d.com/Manual/class-ComputeShader.html) to
 run the neural network within Unity.
 
 ## Supported devices
 
-See the Unity Inference Engine documentation for a list of the
+See the Sentis documentation for a list of the
 [supported platforms](https://docs.unity3d.com/Manual/PlatformSpecific.html).
 
-Scripting Backends : The Unity Inference Engine is generally faster with
+Scripting Backends : Sentis is generally faster with
 **IL2CPP** than with **Mono** for Standalone builds. In the Editor, It is not
-possible to use the Unity Inference Engine with GPU device selected when Editor
+possible to use Sentis with GPU device selected when Editor
 Graphics Emulation is set to **OpenGL(ES) 3.0 or 2.0 emulation**. Also there
 might be non-fatal build time errors when target platform includes Graphics API
 that does not support **Unity Compute Shaders**.
 
-## Using the Unity Inference Engine
+## Using Sentis
 
 When using a model, drag the model file into the **Model** field in the
 Inspector of the Agent. Select the **Inference Device** : CPU or GPU you want to

docs/Training-ML-Agents.md

@@ -122,7 +122,7 @@ artifacts), then use the `--force` flag.
 You can also use this mode to run inference of an already-trained model in
 Python by using both the `--resume` and `--inference` flags. Note that if you
 want to run inference in Unity, you should use the
-[Unity Inference Engine](Getting-Started.md#running-a-pre-trained-model).
+[Sentis](Getting-Started.md#running-a-pre-trained-model).
 
 Additionally, if the network architecture changes, you may still load an existing model,
 but ML-Agents will only load the parts of the model it can load and ignore all others. For instance,

localized_docs/RU/README.md

@@ -49,7 +49,7 @@ Proximal Policy Optimization (PPO) и Soft Actor-Critic (SAC). Первый ал
 - Обучение агента сразу на множестве сцен одновременно. Представьте, как он играет в футбол сразу
 на десяти стадионах, набираясь опыта одновременно на них всех. Выглядит это в Unity также,
 как и представляется.
-- Использование [Unity Inference Engine](docs/Unity-Inference-Engine.md) для поддержки кроссплатформенности.
+- Использование [Sentis](docs/Unity-Inference-Engine.md) для поддержки кроссплатформенности.
 - Контроль через [Python API](docs/Python-API.md) сцен.
 - Возможность обернуть Unity среду для обучения как [gym](gym-unity/README.md).
 

localized_docs/RU/docs/Начало работы.md

@@ -93,7 +93,7 @@ float (с плавающей точкой, например, 3.14), которы
 ## Запуск заранее обученной (предтренированной) модели
 
 Мы включили в свои примеры заранее обученные модели (файлы с расширением `.nn`)
-и использовали [Unity Inference Engine](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Unity-Inference-Engine.md),
+и использовали [Sentis](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Unity-Inference-Engine.md),
 чтобы запустить их в Unity. В этом разделе мы воспользуемся одной
 из таких моделей для 3D Ball.
 

localized_docs/TR/README.md

@@ -29,7 +29,7 @@
 - Rastgele ortamlar kullanarak kararlı ajanlar eğitin
 - İsteğe Bağlı Karar Verme ile esnek ajan kontrolü
 - Birden çok eş zamanlı Unity ortamı örneği kullanarak eğitim
-- [Unity Inference Engine](docs/Unity-Inference-Engine.md) desteği
+- [Sentis](docs/Unity-Inference-Engine.md) desteği
 - [Python API ile](docs/Python-API.md) Unity ortamını kontrol etme
 - Unity - [gym](gym-unity/README.md) desteği
 

localized_docs/TR/docs/Getting-Started.md

@@ -49,7 +49,7 @@ ML-Agent Toolkit, eylemleri iki türe sınıflandırır: sürekli ve ayrık(cont
 
 ## Önceden eğitilmiş modeli çalıştırma
 
-Ajanlarımız (`.nn` dosyaları) için önceden eğitilmiş modeller ortamımıza ekliyoruz  ve bu modelleri Unity içinde çalıştırmak için [Unity Inference Engine]([Unity Inference Engine](Unity-Inference-Engine.md)'i kullanıyoruz. Bu bölümde, 3D Ball örneği için önceden eğitilmiş modeli bir sinir ağı kullanacağız.
+Ajanlarımız (`.nn` dosyaları) için önceden eğitilmiş modeller ortamımıza ekliyoruz  ve bu modelleri Unity içinde çalıştırmak için [Sentis]([Sentis](Unity-Inference-Engine.md)'i kullanıyoruz. Bu bölümde, 3D Ball örneği için önceden eğitilmiş modeli bir sinir ağı kullanacağız.
 
 1. **Project** penceresinde `Assets/ML-Agents/Examples/3DBall/Prefabs` klasörüne gidin. `3DBall`'ın içerisini genişletin ve `Agent` hazır yapısına tıklayın. `Agent` hazır yapısını **Inspector** penceresinde görmelisiniz.
 

localized_docs/TR/docs/Readme.md

@@ -28,7 +28,7 @@
   - [Training Configuration File](Training-Configuration-File.md)
   - [Using TensorBoard to Observe Training](Using-Tensorboard.md)
   - [Profiling Trainers](Profiling-Python.md)
-- [Unity Inference Engine](Unity-Inference-Engine.md)
+- [Sentis](Unity-Inference-Engine.md)
 
 ## Extending ML-Agents