Fixed links and clarified a reference to reset parameters.

docs/Learning-Environment-Design-Brains.md

@@ -4,10 +4,10 @@ The Brain encapsulates the decision making process. Brain objects must be childr
 
 Use the Brain class directly, rather than a subclass. Brain behavior is determined by the **Brain Type**. ML-Agents defines four Brain Types:
 
-* [External](Learning-Environment-External-Internal-Brains.md) — The **External** and **Internal** types typically work together; set **External** when training your agents. You can also use the **External** brain to communicate with a Python script via the Python `UnityEnvironment` class included in the Python portion of the ML-Agents SDK.
-* [Internal](Learning-Environment-External-Internal-Brains.md) – Set **Internal**  to make use of a trained model.
-* [Heuristic](Learning-Environment-Heuristic-Brains.md) – Set **Heuristic** to hand-code the agent's logic by extending the Decision class.
-* [Player](Learning-Environment-Player-Brains.md) – Set **Player** to map keyboard keys to agent actions, which can be useful to test your agent code.
+* [External](Learning-Environment-Design-External-Internal-Brains.md) — The **External** and **Internal** types typically work together; set **External** when training your agents. You can also use the **External** brain to communicate with a Python script via the Python `UnityEnvironment` class included in the Python portion of the ML-Agents SDK.
+* [Internal](Learning-Environment-Design-External-Internal-Brains.md) – Set **Internal**  to make use of a trained model.
+* [Heuristic](Learning-Environment-Design-Heuristic-Brains.md) – Set **Heuristic** to hand-code the agent's logic by extending the Decision class.
+* [Player](Learning-Environment-Design-Player-Brains.md) – Set **Player** to map keyboard keys to agent actions, which can be useful to test your agent code.
 
 During training, set your agent's brain type to **External**. To use the trained model, import the model file into the Unity project and change the brain type to **Internal**. 
 

docs/Python-API.md

@@ -49,7 +49,7 @@ Prints all parameters relevant to the loaded environment and the external brains
 - **Reset : `env.reset(train_model=True, config=None)`**  
 Send a reset signal to the environment, and provides a dictionary mapping brain names to BrainInfo objects.  
     - `train_model` indicates whether to run the environment in train (`True`) or test (`False`) mode.
-    - `config` is an optional dictionary of configuration flags specific to the environment. For more information on adding optional config flags to an environment, see [here](Making-a-new-Unity-Environment.md#implementing-yournameacademy). For generic environments, `config` can be ignored. `config` is a dictionary of strings to floats where the keys are the names of the `resetParameters` and the values are their corresponding float values.  
+    - `config` is an optional dictionary of configuration flags specific to the environment. For generic environments, `config` can be ignored. `config` is a dictionary of strings to floats where the keys are the names of the `resetParameters` and the values are their corresponding float values. Define the reset parameters on the [Academy Inspector](Learning-Environment-Design-Academy.md#academy-properties) window in the Unity Editor.
 - **Step : `env.step(action, memory=None, text_action=None)`**  
 Sends a step signal to the environment using the actions. For each brain : 
     - `action` can be one dimensional arrays or two dimensional arrays if you have multiple agents per brains.

docs/Using-TensorFlow-Sharp-in-Unity.md

@@ -66,7 +66,7 @@ In the Unity Editor, you must specify the names of the nodes used by your graph
 
 ![Internal Brain Inspector](images/internal_brain.png)
 
-See [Internal Brain](Learning-Environments-Internal-Brains.md) for more information about using the InternalBrain object.
+See [Internal Brain](Learning-Environments-Design-External-Internal-Brains.md#internal-brain) for more information about using Internal Brains.
 
 If you followed these instructions well, the agents in your environment that use this brain will use your fully trained network to make decisions.