Updating curriculum learning docs.

- Docs updated for multi-brain curriculums.

docs/Training-Curriculum-Learning.md

 accomplish the task. From there, we can slowly add to the difficulty of the task by
 increasing the size of the wall, until the agent can complete the initially
 near-impossible task of scaling the wall. We are including just such an environment with
-the ML-Agents toolkit 0.2, called Wall Jump.
+the ML-Agents toolkit 0.2, called __Wall Jump__.
 
 ![Wall](images/curriculum.png)
 
 
 ## How-To
 
+Each Brain in an environment can have a corresponding curriculum. These
+curriculums are held in what we call a metacurriculum. A metacurriculum allows
+different brains to follow different curriculums within the same environment.
+
+### Specifying a Metacurriculum
+
+We first create a folder inside `python/curricula/` for the environment we want
+to use curriculum learning with. For example, if we were creating a metacurriculum
+for Wall Jump, we would create the folder `python/curricula/wall-jump/`. We will place
+our curriculums inside this folder.
+
+### Specifying a Curriculum
+
-parameters of the environment will vary. In the case of the Wall Area environment, what
+parameters of the environment will vary. In the case of the Wall Jump environment, what
-than adjusting it by hand, we then create a simple JSON file which describes the
-structure of the curriculum. Within it we can set at what points in the training process
+than adjusting it by hand, we will create a simple JSON file which describes the
+structure of the curriculum. Within it, we can specify which points in the training process
-Finally, we have to use the reset parameter we defined and modify the environment from
-the agent's `AgentReset()` function.
-Once these are in place, we simply launch learn.py using the `–curriculum` flag to
-point to the JSON file, and PPO we will train using Curriculum Learning. Of course we can
-then keep track of the current lesson and progress via TensorBoard.
-
+Below is an example curriculum for the BigWallBrain in the Wall Jump environment.
 
 ```json
 {
     "parameters" :
     {
         "big_wall_min_height" : [0.0, 4.0, 6.0, 8.0],
-        "big_wall_max_height" : [4.0, 7.0, 8.0, 8.0],
-        "small_wall_height" : [1.5, 2.0, 2.5, 4.0]
+        "big_wall_max_height" : [4.0, 7.0, 8.0, 8.0]
     }
 }
 ```
     * If `true`, weighting will be 0.75 (new) 0.25 (old).
 * `parameters` (dictionary of key:string, value:float array) - Corresponds to academy reset parameters to control. Length of each array
 should be one greater than number of thresholds.
+
+
+Once our curriculum is defined, we have to use the reset parameters we defined and modify
+the environment from the agent's `AgentReset()` function. See
+[WallJumpAgent.cs](https://github.com/Unity-Technologies/ml-agents/blob/master/unity-environment/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs)
+for an example.
+
+We will save this file into our metacurriculum folder with the name of its
+corresponding Brain. For example, in the Wall Jump environment, there are
+two brains---BigWallBrain and SmallWallBrain. If we want to define a
+curriculum for the BigWallBrain, we will save `BigWallBrain.json` into
+`python/curricula/wall-jump/`.
+
+### Training with a Curriculum
+
+Once we have specified our metacurriculum and curriculums, we can launch `learn.py` using the `–curriculum`
+flag to point to the metacurriculum folder and PPO will train using Curriculum Learning. For example,
+to train agents in the Wall Jump environment with curriculum learning, we can run
+`python learn.py --curriculum=curricula/wall-jump/ --run-id=wall-jump-curriculum --train`.
+We can then keep track of the current lessons and progresses via TensorBoard.