Fix typo in documentation (#1516)

* Fix typo
* Fix line lengths

docs/Training-Imitation-Learning.md

@@ -12,17 +12,31 @@ from a demonstration to learn a policy. [Video Link](https://youtu.be/kpb8ZkMBFY
 
 ## Recording Demonstrations
 
-It is possible to record demonstrations of agent behavior from the Unity Editor, and save them as assets. These demonstrations contain information on the observations, actions, and rewards for a given agent during the recording session. They can be managed from the Editor, as well as used for training with Offline Behavioral Cloning (see below).
+It is possible to record demonstrations of agent behavior from the Unity Editor, 
+and save them as assets. These demonstrations contain information on the 
+observations, actions, and rewards for a given agent during the recording session. 
+They can be managed from the Editor, as well as used for training with Offline 
+Behavioral Cloning (see below).
 
-In order to record demonstrations from an agent, add the `Demonstration Recorder` component to a GameObject in the scene which contains an `Agent` component. Once added, it is possible to name the demonstration that will be recorded from the agent.
+In order to record demonstrations from an agent, add the `Demonstration Recorder` 
+component to a GameObject in the scene which contains an `Agent` component. 
+Once added, it is possible to name the demonstration that will be recorded 
+from the agent.
 
 <p align="center">
   <img src="images/demo_component.png"
        alt="BC Teacher Helper"
        width="375" border="10" />
 </p>
 
-When `Record` is checked, a demonstration will be created whenever the scene is played from the Editor. Depending on the complexity of the task, anywhere from a few minutes or a few hours of demonstration data may be necessary to be useful for imitation learning. When you have recorded enough data, end the Editor play session, and a `.demo` file will be created in the `Assets/Demonstrations` folder. This file contains the demonstrations. Clicking on the file will provide metadata about the demonstration in the inspector.
+When `Record` is checked, a demonstration will be created whenever the scene 
+is played from the Editor. Depending on the complexity of the task, anywhere 
+from a few minutes or a few hours of demonstration data may be necessary to 
+be useful for imitation learning. When you have recorded enough data, end 
+the Editor play session, and a `.demo` file will be created in the 
+`Assets/Demonstrations` folder. This file contains the demonstrations. 
+Clicking on the file will provide metadata about the demonstration in the 
+inspector.
 
 <p align="center">
   <img src="images/demo_inspector.png"
@@ -33,29 +47,42 @@ When `Record` is checked, a demonstration will be created whenever the scene is
 
 ## Training with Behavioral Cloning
 
-There are a variety of possible imitation learning algorithms which can be used,
-the simplest one of them is Behavioral Cloning. It works by collecting demonstrations from a teacher, and then simply uses them to directly learn a policy, in the
-same way the supervised learning for image classification or other traditional
-Machine Learning tasks work.
+There are a variety of possible imitation learning algorithms which can 
+be used, the simplest one of them is Behavioral Cloning. It works by collecting 
+demonstrations from a teacher, and then simply uses them to directly learn a 
+policy, in the same way the supervised learning for image classification 
+or other traditional Machine Learning tasks work.
 
 
 ### Offline Training
 
-With offline behavioral cloning, we can use demonstrations (`.demo` files) generated using the `Demonstration Recorder` as the dataset used to train a behavior.
+With offline behavioral cloning, we can use demonstrations (`.demo` files) 
+generated using the `Demonstration Recorder` as the dataset used to train a behavior.
 
 1. Choose an agent you would like to learn to imitate some set of demonstrations. 
-2. Record a set of demonstration using the `Demonstration Recorder` (see above). For illustrative purposes we will refer to this file as `AgentRecording.demo`. 
-3. Build the scene, assigning the agent a Learning Brain, and set the Brain to Control in the Broadcast Hub. For more information on Brains, see [here](Learning-Environment-Design-Brains.md).
+2. Record a set of demonstration using the `Demonstration Recorder` (see above). 
+   For illustrative purposes we will refer to this file as `AgentRecording.demo`. 
+3. Build the scene, assigning the agent a Learning Brain, and set the Brain to 
+   Control in the Broadcast Hub. For more information on Brains, see 
+   [here](Learning-Environment-Design-Brains.md).
 4. Open the `config/offline_bc_config.yaml` file. 
-5. Modify the `demo_path` parameter in the file to reference the path to the demonstration file recorded in step 2. In our case this is: `./UnitySDK/Assets/Demonstrations/AgentRecording.demo`
-6. Launch `mlagent-learn`, providing `./config/offline_bc_config.yaml` as the config parameter, and include the `--run-id` and `--train` as usual. Provide your environment as the `--env` parameter if it has been compiled as standalone, or omit to train in the editor.
+5. Modify the `demo_path` parameter in the file to reference the path to the 
+   demonstration file recorded in step 2. In our case this is: 
+   `./UnitySDK/Assets/Demonstrations/AgentRecording.demo`
+6. Launch `mlagent-learn`, providing `./config/offline_bc_config.yaml` 
+   as the config parameter, and include the `--run-id` and `--train` as usual. 
+   Provide your environment as the `--env` parameter if it has been compiled 
+   as standalone, or omit to train in the editor.
 7. (Optional) Observe training performance using Tensorboard.
 
-This will use the demonstration file to train a neural network driven agent to directly imitate the actions provided in the demonstration. The environment will launch and be used for evaluating the agent's performance during training.
+This will use the demonstration file to train a neural network driven agent 
+to directly imitate the actions provided in the demonstration. The environment 
+will launch and be used for evaluating the agent's performance during training.
 
 ### Online Training
 
-It is also possible to provide demonstrations in realtime during training, without pre-recording a demonstration file. The steps to do this are as follows:
+It is also possible to provide demonstrations in realtime during training, 
+without pre-recording a demonstration file. The steps to do this are as follows:
 
 1. First create two Brains, one which will be the "Teacher," and the other which
    will be the "Student." We will assume that the names of the Brain
@@ -65,27 +92,27 @@ It is also possible to provide demonstrations in realtime during training, witho
 3. The "Student" Brain must be a **Learning Brain**.
 4. The Brain Parameters of both the "Teacher" and "Student" Brains must be 
    compatible with the agent.
-5. Drag both the "Teacher" and "Student" Brain into the Academy's `Broadcast Hub`
+5. Drag both the "Teacher" and "Student" Brain into the Academy's `Broadcast Hub` 
    and check the `Control` checkbox on the "Student" Brain. 
-4. Link the Brains to the desired Agents (one Agent as the teacher and at least
+6. Link the Brains to the desired Agents (one Agent as the teacher and at least
    one Agent as a student).
-5. In `config/online_bc_config.yaml`, add an entry for the "Student" Brain. Set
-   the `trainer` parameter of this entry to `imitation`, and the
+7. In `config/online_bc_config.yaml`, add an entry for the "Student" Brain. Set
+   the `trainer` parameter of this entry to `online_bc`, 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 `mlagents-learn config/online_bc_config.yaml
+8. Launch the training process with `mlagents-learn config/online_bc_config.yaml
    --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
+9. 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
+10. 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
+11. Once the Student Agents are exhibiting the desired behavior, end the training
    process with `CTL+C` from the command line.
-10. Move the resulting `*.bytes` file into the `TFModels` subdirectory of the
+12. Move the resulting `*.bytes` file into the `TFModels` subdirectory of the
     Assets folder (or a subdirectory within Assets of your choosing) , and use
     with `Learning` Brain.