Replaced message printed in Python and in documentation. (#881)

docs/Basic-Guide.md

 Where:
 - `<run-identifier>` is a string used to separate the results of different training runs
 - And the `--train` tells learn.py to run a training session (rather than inference)
-5. When the message _"Ready to connect with the Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor.
+5. When the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor.
 
 **Note**: Alternatively, you can use an executable rather than the Editor to perform training. Please refer to [this page](Learning-Environment-Executable.md) for instructions on how to build and use an executable.
 

docs/Getting-Started-with-Balance-Ball.md

 python3 learn.py --run-id=<run-identifier> --train 
 ```
 
-When the message _"Ready to connect with the Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor.
+When the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor.
 
 **Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.
 

docs/Python-API.md

 * `worker_id` indicates which port to use for communication with the environment. For use in parallel training regimes such as A3C.
 * `seed` indicates the seed to use when generating random numbers during the training process. In environments which do not involve physics calculations, setting the seed enables reproducible experimentation by ensuring that the environment and trainers utilize the same random seed.
 
-If you want to directly interact with the Editor, you need to use `file_name=None`, then press the :arrow_forward: button in the Editor when the message _"Ready to connect with the Editor"_ is displayed on the screen
+If you want to directly interact with the Editor, you need to use `file_name=None`, then press the :arrow_forward: button in the Editor when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen
 
 ## Interacting with a Unity Environment
 

docs/Training-Imitation-Learning.md

 3. Set the "Student" brain to External mode.
 4. Link the brains to the desired agents (one agent as the teacher and at least one agent as a student).
 5. In `trainer_config.yaml`, add an entry for the "Student" brain. Set the `trainer` parameter of this entry to `imitation`, and the `brain_to_imitate` parameter to the name of the teacher brain: "Teacher". Additionally, set `batches_per_epoch`, which controls how much training to do each moment. Increase the `max_steps` option if you'd like to keep training the agents for a longer period of time.
-6. Launch the training process with `python3 python/learn.py --train --slow`, and press the :arrow_forward: button in Unity when the message _"Ready to connect with the Editor"_ is displayed on the screen
+6. Launch the training process with `python3 python/learn.py --train --slow`, and press the :arrow_forward: button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen
 7. From the Unity window, control the agent with the Teacher brain by providing "teacher demonstrations" of the behavior you would like to see.
 8. Watch as the agent(s) with the student brain attached begin to behave similarly to the demonstrations.
 9. Once the Student agents are exhibiting the desired behavior, end the training process with `CTL+C` from the command line.

docs/Training-ML-Agents.md

     python3 learn.py <env_name> --run-id=<run-identifier> --train
 
 where 
- * `<env_name>`__(Optional)__ is the name (including path) of your Unity executable containing the agents to be trained. If `<env_name>` is not passed, the training will happen in the Editor. Press the :arrow_forward: button in Unity when the message _"Ready to connect with the Editor"_ is displayed on the screen.
+ * `<env_name>`__(Optional)__ is the name (including path) of your Unity executable containing the agents to be trained. If `<env_name>` is not passed, the training will happen in the Editor. Press the :arrow_forward: button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen.
  * `<run-identifier>` is an optional identifier you can use to identify the results of individual training runs.
 
 For example, suppose you have a project in Unity named "CatsOnBicycles" which contains agents ready to train. To perform the training:

docs/Using-Docker.md

 Notes on argument values:
 - `<container-name>` is used to identify the container (in case you want to interrupt and terminate it). This is optional and Docker will generate a random name if this is not set. _Note that this must be unique for every run of a Docker image._
 - `<image-name>` references the image name used when building the container.
-- `<environemnt-name>` __(Optional)__: If you are training with a linux executable, this is the name of the executable. If you are training in the Editor, do not pass a `<environemnt-name>` argument and press the :arrow_forward: button in Unity when the message _"Ready to connect with the Editor"_ is displayed on the screen.
+- `<environemnt-name>` __(Optional)__: If you are training with a linux executable, this is the name of the executable. If you are training in the Editor, do not pass a `<environemnt-name>` argument and press the :arrow_forward: button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen.
 - `source`: Reference to the path in your host OS where you will store the Unity executable. 
 - `target`: Tells Docker to mount the `source` path as a disk with this name. 
 - `docker-target-name`: Tells the ML-Agents Python package what the name of the disk where it can read the Unity executable and store the graph. **This should therefore be identical to `target`.**

python/unityagents/environment.py

         if file_name is not None:
             self.executable_launcher(file_name, docker_training, no_graphics)
         else:
-            logger.info("Ready to connect with the Editor.")
+            logger.info("Start training by pressing the Play button in the Unity Editor.")
         self._loaded = True
 
         rl_init_parameters_in = UnityRLInitializationInput(