Doc/comment cleanup - Fix some occurrences of 'the the' (#2119)

.gitignore

 # Ignore PyPi build files.
 dist/
 build/
+
+# Python virtual environment
+venv/

UnitySDK/Assets/ML-Agents/Editor/HeuristicBrainEditor.cs

                 {
                     Debug.LogError(
                         "Instance of " + brain.decisionScript.name + " couldn't be created. " +
-                        "The the script class needs to derive from Decision.");
+                        "The script class needs to derive from Decision.");
                     brain.decisionScript = null;
                 }
             }

docs/Basic-Guide.md

 1. Launch Unity
 2. On the Projects dialog, choose the **Open** option at the top of the window.
 3. Using the file dialog that opens, locate the `UnitySDK` folder
-   within the the ML-Agents toolkit project and click **Open**.
+   within the ML-Agents toolkit project and click **Open**.
 4. Go to **Edit** > **Project Settings** > **Player**
 5. For **each** of the platforms you target (**PC, Mac and Linux Standalone**,
    **iOS** or **Android**):

docs/Glossary.md

 * **Editor** - The Unity Editor, which may include any pane (e.g. Hierarchy,
   Scene, Inspector).
 * **Environment** - The Unity scene which contains Agents, Academy, and Brains.
-* **FixedUpdate** - Unity method called each time the the game engine is
+* **FixedUpdate** - Unity method called each time the game engine is
   stepped. ML-Agents logic should be placed here.
 * **Frame** - An instance of rendering the main camera for the display.
   Corresponds to each `Update` call of the game engine.

docs/Training-on-Microsoft-Azure.md

 
 1. [Move](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/copy-files-to-linux-vm-using-scp)
     your built Unity application to your Virtual Machine.
-2. Set the the directory where the ML-Agents Toolkit was installed to your
+2. Set the directory where the ML-Agents Toolkit was installed to your
    working directory.
 3. Run the following command:
 

ml-agents/mlagents/trainers/bc/offline_trainer.py

         :param training: Whether the trainer is set for training.
         :param load: Whether the model should be loaded.
         :param seed: The seed the model will be initialized with
-        :param run_id: The The identifier of the current run
+        :param run_id: The identifier of the current run
         """
         super(OfflineBCTrainer, self).__init__(
             brain, trainer_parameters, training, load, seed, run_id

ml-agents/mlagents/trainers/bc/online_trainer.py

         :param training: Whether the trainer is set for training.
         :param load: Whether the model should be loaded.
         :param seed: The seed the model will be initialized with
-        :param run_id: The The identifier of the current run
+        :param run_id: The identifier of the current run
         """
         super(OnlineBCTrainer, self).__init__(
             brain, trainer_parameters, training, load, seed, run_id

ml-agents/mlagents/trainers/bc/trainer.py

         :param training: Whether the trainer is set for training.
         :param load: Whether the model should be loaded.
         :param seed: The seed the model will be initialized with
-        :param run_id: The The identifier of the current run
+        :param run_id: The identifier of the current run
         """
         super(BCTrainer, self).__init__(brain, trainer_parameters, training, run_id)
         self.policy = BCPolicy(seed, brain, trainer_parameters, load)

ml-agents/mlagents/trainers/ppo/trainer.py

         :param training: Whether the trainer is set for training.
         :param load: Whether the model should be loaded.
         :param seed: The seed the model will be initialized with
-        :param run_id: The The identifier of the current run
+        :param run_id: The identifier of the current run
         """
         super(PPOTrainer, self).__init__(brain, trainer_parameters, training, run_id)
         self.param_keys = [