Correct reference inconsistency

Author: maryamziaa

com.unity.ml-agents/Documentation~/Learning-Environment-Create-New.md

@@ -1,7 +1,7 @@
 # Making a New Learning Environment
 
 This tutorial walks through the process of creating a Unity Environment from
-scratch. We recommend first reading the [running an example](Sample.md)
+scratch. We recommend first reading the [Running an Example](Sample.md)
 guide to understand the concepts presented here first in an already-built
 environment.
 
@@ -378,7 +378,7 @@ from the platform.
 ## Training the Environment
 
 The process is the same as described in the
-[sample guide](Sample.md).
+[Running an Example](Sample.md) guide.
 
 The hyperparameters for training are specified in a configuration file that you
 pass to the `mlagents-learn` program. Create a new `rollerball_config.yaml` file

com.unity.ml-agents/Documentation~/ML-Agents-Overview.md

@@ -224,7 +224,7 @@ being sent to the Python API, they will be fed into their (internal, embedded)
 model to generate the _optimal_ action for each medic to take at every point in
 time.
 
-The [Getting Started Guide](Sample.md) tutorial covers this training
+The [Running an Example](Sample.md) guide covers this training
 mode with the **3D Balance Ball** sample environment.
 
 #### Cross-Platform Inference

com.unity.ml-agents/Documentation~/Readme.md

@@ -45,7 +45,7 @@ descriptions of all these features. Or go straight to our [web docs](https://uni
 ## Releases & Documentation
 
 **Our latest, stable release is `Release 22`. Click
-[here](Getting-Started.md)
+[here](Sample.md)
 to get started with the latest release of ML-Agents.**
 
 **You can also check out our new [web docs](https://unity-technologies.github.io/ml-agents/)!**

com.unity.ml-agents/Documentation~/Training-ML-Agents.md

@@ -51,8 +51,8 @@ where
   your training runs.
 
 See the
-[Getting Started Guide](Sample.md#training-a-new-model-with-reinforcement-learning)
-for a sample execution of the `mlagents-learn` command.
+[Running an Example](Sample.md#training-a-new-model-with-reinforcement-learning)
+guide for a sample execution of the `mlagents-learn` command.
 
 #### Observing Training
 
@@ -132,7 +132,7 @@ process when the default parameters don't seem to be giving the level of
 performance you would like. We provide sample configuration files for our
 example environments in the [config/](../../config/) directory. The
 `config/ppo/3DBall.yaml` was used to train the 3D Balance Ball in the
-[Getting Started](Sample.md) guide. That configuration file uses the
+[Running an Example](Sample.md) guide. That configuration file uses the
 PPO trainer, but we also have configuration files for SAC and GAIL.
 
 Additionally, the set of configurations you provide depend on the training

com.unity.ml-agents/Documentation~/Using-Docker.md

@@ -48,7 +48,7 @@ Using Docker for ML-Agents involves three steps: building the Unity environment
 with specific flags, building a Docker container and, finally, running the
 container. If you are not familiar with building a Unity environment for
 ML-Agents, please read through our
-[Getting Started with the 3D Balance Ball Example](Getting-Started.md) guide
+[Getting Started with the 3D Balance Ball Example](Sample.md) guide
 first.
 
 ### Build the Environment (Optional)