Unity-Technologies · vincentpierre · Mar 30, 2020 · Mar 27, 2020 · Mar 27, 2020 · Mar 30, 2020
diff --git a/com.unity.ml-agents/CHANGELOG.md b/com.unity.ml-agents/CHANGELOG.md
@@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 ## [Unreleased]
 ### Major Changes
+ - The Jupyter notebooks have been removed from the repository.
 
 ### Minor Changes
  - Format of console output has changed slightly and now matches the name of the model/summary directory. (#3630, #3616)

diff --git a/docs/Background-Jupyter.md b/docs/Background-Jupyter.md
diff --git a/docs/Installation.md b/docs/Installation.md
@@ -103,8 +103,7 @@ parameters you can use with `mlagents-learn`.
 
 By installing the `mlagents` package, the dependencies listed in the
 [setup.py file](../ml-agents/setup.py) are also installed. These include
-[TensorFlow](Background-TensorFlow.md) (Requires a CPU w/ AVX support) and
-[Jupyter](Background-Jupyter.md).
+[TensorFlow](Background-TensorFlow.md) (Requires a CPU w/ AVX support).
 
 #### Advanced: Installing for Development
 

diff --git a/docs/Learning-Environment-Create-New.md b/docs/Learning-Environment-Create-New.md
@@ -388,11 +388,6 @@ from the platform. Note that for more involved debugging, the ML-Agents SDK
 includes a convenient Monitor class that you can use to easily display Agent
 status information in the Game window.
 
-One additional test you can perform is to first ensure that your environment and
-the Python API work as expected using the `notebooks/getting-started.ipynb`
-[Jupyter notebook](Background-Jupyter.md). Within the notebook, be sure to set
-`env_name` to the name of the environment file you specify when building this
-environment.
 
 ## Training the Environment
 

diff --git a/docs/Migrating.md b/docs/Migrating.md
@@ -10,6 +10,7 @@ The versions can be found in
 ## Migrating from 0.15 to latest
 
 ### Important changes
+* The Jupyter notebooks have been removed from the repository.
 
 ### Steps to Migrate
 

diff --git a/docs/Python-API.md b/docs/Python-API.md
@@ -15,8 +15,7 @@ As such, it can serve as the basis for developing and evaluating new learning al
 The ML-Agents Toolkit Low Level API is a Python API for controlling the simulation
 loop of an environment or game built with Unity. This API is used by the
 training algorithms inside the ML-Agent Toolkit, but you can also write your own
-Python programs using this API. Go [here](../notebooks/getting-started.ipynb)
-for a Jupyter Notebook walking through the functionality of the API.
+Python programs using this API.
 
 The key objects in the Python API include:
 

diff --git a/docs/Readme.md b/docs/Readme.md
@@ -3,7 +3,6 @@
 ## Installation & Set-up
 
 * [Installation](Installation.md)
-  * [Background: Jupyter Notebooks](Background-Jupyter.md)
   * [Using Virtual Environment](Using-Virtual-Environment.md)
 
 ## Getting Started

diff --git a/gym-unity/README.md b/gym-unity/README.md
@@ -58,8 +58,6 @@ env = UnityEnv(environment_filename, worker_id, use_visual, uint8_visual, multia
 
 The returned environment `env` will function as a gym.
 
-For more on using the gym interface, see our
-[Jupyter Notebook tutorial](../notebooks/getting-started-gym.ipynb).
 
 ## Limitations
 

diff --git a/ml-agents/setup.py b/ml-agents/setup.py
@@ -56,7 +56,6 @@ def run(self):
         # Test-only dependencies should go in test_requirements.txt, not here.
         "grpcio>=1.11.0",
         "h5py>=2.9.0",
-        "jupyter",
         "matplotlib",
         "mlagents_envs=={}".format(VERSION),
         "numpy>=1.13.3,<2.0",

diff --git a/notebooks/getting-started-gym.ipynb b/notebooks/getting-started-gym.ipynb