removing broken links

surfnerd · Oct 1, 2019 · 60dd1de · 60dd1de
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diff --git a/docs/Installation.md b/docs/Installation.md
@@ -16,12 +16,8 @@ Build Support_ component when installing Unity.
        width="500" border="10" />
 </p>
 
-## Windows Users
-For setting up your environment on Windows, we have created a [detailed
-guide](Installation-Windows.md) to setting up your env. For Mac and Linux,
-continue with this guide.
-
-## Mac and Unix Users
+## Environment Setup
+For setting up your environment follow this [guide](Using-Virtual-Environment.md).
 
 ### Clone the ML-Agents Toolkit Repository
 
@@ -94,12 +90,7 @@ pip3 install -e ./
 Running pip with the `-e` flag will let you make changes to the Python files directly and have those
 reflected when you run `mlagents-learn`. It is important to install these packages in this order as the
 `mlagents` package depends on `mlagents_envs`, and installing it in the other 
-order will download `mlagents_envs` from PyPi. 
-
-## Docker-based Installation
-
-If you'd like to use Docker for ML-Agents, please follow
-[this guide](Using-Docker.md).
+order will download `mlagents_envs` from PyPi.
 
 ## Next Steps
 

diff --git a/docs/ML-Agents-Overview.md b/docs/ML-Agents-Overview.md
@@ -430,10 +430,6 @@ training process.
   the broadcasting feature
   [here](Learning-Environment-Design-Brains.md#using-the-broadcast-feature).
 
-- **Docker Set-up (Experimental)** - To facilitate setting up ML-Agents without
-  installing Python or TensorFlow directly, we provide a
-  [guide](Using-Docker.md) on how to create and run a Docker container.
-
 - **Cloud Training on AWS** - To facilitate using the ML-Agents toolkit on
   Amazon Web Services (AWS) machines, we provide a
   [guide](Training-on-Amazon-Web-Service.md) on how to set-up EC2 instances in

diff --git a/docs/Training-on-Microsoft-Azure.md b/docs/Training-on-Microsoft-Azure.md
@@ -107,9 +107,5 @@ training](Using-Tensorboard.md).
 [Azure Container Instances](https://azure.microsoft.com/services/container-instances/)
 allow you to spin up a container, on demand, that will run your training and
 then be shut down.  This ensures you aren't leaving a billable VM running when
-it isn't needed.  You can read more about
-[The ML-Agents toolkit support for Docker containers here](Using-Docker.md).
-Using ACI enables you to offload training of your models without needing to
-install Python and TensorFlow on your own computer.  You can find instructions,
-including a pre-deployed image in DockerHub for you to use, available
-[here](https://github.com/druttka/unity-ml-on-azure).
+it isn't needed. Using ACI enables you to offload training of your models without needing to
+install Python and TensorFlow on your own computer.