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Chris Elion
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Doc/comment cleanup - Fix some occurrences of 'the the' (#2119)
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.gitignore

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@@ -97,3 +97,6 @@ ml-agents-protobuf/Grpc*
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# Ignore PyPi build files.
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dist/
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build/
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# Python virtual environment
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venv/

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

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@@ -47,7 +47,7 @@ private static void CheckIsDecision(HeuristicBrain brain)
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{
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Debug.LogError(
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"Instance of " + brain.decisionScript.name + " couldn't be created. " +
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"The the script class needs to derive from Decision.");
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"The script class needs to derive from Decision.");
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brain.decisionScript = null;
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}
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}

docs/Basic-Guide.md

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@@ -16,7 +16,7 @@ Unity settings.
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1. Launch Unity
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2. On the Projects dialog, choose the **Open** option at the top of the window.
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3. Using the file dialog that opens, locate the `UnitySDK` folder
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within the the ML-Agents toolkit project and click **Open**.
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within the ML-Agents toolkit project and click **Open**.
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4. Go to **Edit** > **Project Settings** > **Player**
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5. For **each** of the platforms you target (**PC, Mac and Linux Standalone**,
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**iOS** or **Android**):

docs/Glossary.md

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

docs/Training-on-Microsoft-Azure.md

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@@ -66,7 +66,7 @@ To run your training on the VM:
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1. [Move](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/copy-files-to-linux-vm-using-scp)
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your built Unity application to your Virtual Machine.
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2. Set the the directory where the ML-Agents Toolkit was installed to your
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2. Set the directory where the ML-Agents Toolkit was installed to your
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working directory.
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3. Run the following command:
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ml-agents/mlagents/trainers/bc/offline_trainer.py

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@@ -22,7 +22,7 @@ def __init__(self, brain, trainer_parameters, training, load, seed, run_id):
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:param training: Whether the trainer is set for training.
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:param load: Whether the model should be loaded.
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:param seed: The seed the model will be initialized with
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:param run_id: The The identifier of the current run
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:param run_id: The identifier of the current run
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"""
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super(OfflineBCTrainer, self).__init__(
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brain, trainer_parameters, training, load, seed, run_id

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

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@@ -21,7 +21,7 @@ def __init__(self, brain, trainer_parameters, training, load, seed, run_id):
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:param training: Whether the trainer is set for training.
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:param load: Whether the model should be loaded.
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:param seed: The seed the model will be initialized with
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:param run_id: The The identifier of the current run
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:param run_id: The identifier of the current run
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"""
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super(OnlineBCTrainer, self).__init__(
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brain, trainer_parameters, training, load, seed, run_id

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

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@@ -25,7 +25,7 @@ def __init__(self, brain, trainer_parameters, training, load, seed, run_id):
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:param training: Whether the trainer is set for training.
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:param load: Whether the model should be loaded.
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:param seed: The seed the model will be initialized with
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:param run_id: The The identifier of the current run
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:param run_id: The identifier of the current run
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"""
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super(BCTrainer, self).__init__(brain, trainer_parameters, training, run_id)
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self.policy = BCPolicy(seed, brain, trainer_parameters, load)

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

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

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