added the note about the windows bug (#1309)

* added the note about the windows bug

* made them uniform

* added where the parameter is

* resolved the comments

* added one space

docs/Basic-Guide.md

 You can press Ctrl+C to stop the training, and your trained model will be at
 `models/<run-identifier>/editor_<academy_name>_<run-identifier>.bytes` where
 `<academy_name>` is the name of the Academy GameObject in the current scene.
-This file corresponds to your model's latest checkpoint. You can now embed this
-trained model into your Learning Brain by following the steps below, which is
-similar to the steps described
+(**Note:** There is a known bug on Windows that causes the saving of the model to
+fail when you early terminate the training, it's recommended to wait until Step
+has reached the max_steps parameter you set in trainer_config.yaml.) This file
+corresponds to your model's latest checkpoint. You can now embed this trained
+model into your Learning Brain by following the steps below, which is similar to
+the steps described
 [above](#play-an-example-environment-using-pretrained-model).
 
 1. Move your model file into

docs/Learning-Environment-Executable.md

 
 You can press Ctrl+C to stop the training, and your trained model will be at
 `models/<run-identifier>/<env_name>_<run-identifier>.bytes`, which corresponds
-to your model's latest checkpoint. You can now embed this trained model into
-your Learning Brain by following the steps below:
+to your model's latest checkpoint. (**Note:** There is a known bug on Windows
+that causes the saving of the model to fail when you early terminate the
+training, it's recommended to wait until Step has reached the max_steps
+parameter you set in trainer_config.yaml.) You can now embed this trained model
+into your Learning Brain by following the steps below:
 
 1. Move your model file into
    `UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/`.

docs/Training-ML-Agents.md

 under the assigned run-id — in the cats example, the path to the model would be
 `models/cob_1/CatsOnBicycles_cob_1.bytes`.
 
-While this example used the default training hyperparameters, you can edit the
+On Mac and Linux platform, you can press Ctrl+c to terminate your training
+early, the model will be saved as if you set your max_steps to the current step.
+(**Note:** There is a known bug on Windows that causes the saving of the model
+to fail when you early terminate the training, it's recommended to wait until
+Step has reached the max_steps parameter you set in trainer_config.yaml.) While
+this example used the default training hyperparameters, you can edit the
 [training_config.yaml file](#training-config-file) with a text editor to set
 different values.