Additional documentation changes

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

 **Vector Observation Space**
 
 Before making a decision, an agent collects its observation about its state 
-in the world. ML-Agents classifies vector observations into two types:
-**Continuous** and **Discrete**. The **Continuous** vector observation space 
-collects observations in a vector of floating point numbers. The **Discrete** 
-vector observation space is an index into a table of states. Most of the example
-environments use a continuous vector observation space. 
+in the world. The vector observation is a vector of floating point numbers
+which contain relevant information for the agent to make decisions. 
 
 The Brain instance used in the 3D Balance Ball example uses the **Continuous** 
 vector observation space with a **State Size** of 8. This means that the 

docs/Learning-Environment-Design-Agents.md

  
 ### Multiple Visual Observations
 
-Camera observations use rendered textures from one or more cameras in a scene. The brain vectorizes the textures into a 3D Tensor which can be fed into a convolutional neural network (CNN). For more information on CNNs, see [this guide](http://cs231n.github.io/convolutional-networks/). You can use camera observations and either continuous feature vector or discrete state observations at the same time.
+Camera observations use rendered textures from one or more cameras in a scene. The brain vectorizes the textures into a 3D Tensor which can be fed into a convolutional neural network (CNN). For more information on CNNs, see [this guide](http://cs231n.github.io/convolutional-networks/). You can use camera observations along side vector observations.
  
 Agents using camera images can capture state of arbitrary complexity and are useful when the state is difficult to describe numerically. However, they are also typically less efficient and slower to train, and sometimes don't succeed at all.  
 

docs/Learning-Environment-Design-Brains.md

 
 * `Brain Parameters` - Define vector observations, visual observation, and vector actions for the Brain.
     * `Vector Observation` 
-    	* `Space Size` - Length of vector observation for brain (In _Continuous_ space type). Or number of possible values (in _Discrete_ space type).
+    	* `Space Size` - Length of vector observation for brain.
 		* `Stacked Vectors` - The number of previous vector observations that will be stacked and used collectively for decision making. This results in the effective size of the vector observation being passed to the brain being: _Space Size_ x _Stacked Vectors_.
 	* `Visual Observations`	- Describes height, width, and whether to grayscale visual observations for the Brain.
 	* `Vector Action`

docs/ML-Agents-Overview.md

 Observations can be numeric and/or visual. Numeric observations measure 
 attributes of the environment from the point of view of the agent. For
 our medic this would be attributes of the battlefield that are visible to it.
-Observations can either be _discrete_ or _continuous_ depending on the complexity
-of the game and agent. For most interesting environments, an agent will require 
-several continuous numeric observations, while for simple environments with 
-a small number of unique configurations, a discrete observation will suffice. 
+For most interesting environments, an agent will require 
+several continuous numeric observations. 
 Visual observations, on the other hand, are images generated from the cameras 
 attached to the agent and represent what the agent is seeing at that point
 in time. It is common to confuse an agent's observation with the environment

docs/Python-API.md

 A BrainInfo object contains the following fields:
 
 * **`visual_observations`** : A list of 4 dimensional numpy arrays. Matrix n of the list corresponds to the n<sup>th</sup> observation of the brain. 
-* **`vector_observations`** : A two dimensional numpy array of dimension `(batch size, vector observation size)` if the vector observation space is continuous and `(batch size, 1)` if the vector observation space is discrete.
+* **`vector_observations`** : A two dimensional numpy array of dimension `(batch size, vector observation size)`.
 * **`text_observations`** : A list of string corresponding to the agents text observations.
 * **`memories`** : A two dimensional numpy array of dimension `(batch size, memory size)` which corresponds to the memories sent at the previous step.
 * **`rewards`** : A list as long as the number of agents using the brain containing the rewards they each obtained at the previous step.