merge release 0.15.0 into master (#3652)

* Bumping version on the release (#3615)

* Update examples project to 2018.4.18f1 (#3618)

From 2018.4.14f1.  An internal package dependency was updated as
a side effect.

* Remove dead components from the examples scenes (#3619) (#3624)

* Improve warnings and exception if using unsupported combo

* add meta file

* fix unit test

* enforce onnx conversion (expect tf2 CI to fail) (#3600)

* Update error message

* Updated the release branch docs (#3621)

* Updated the release branch docs

* Edited the README

* make sure top-level timer is closed before writing

* Remove space from Product Name for examples

In #2588 it was suggested that the space in the Product Name for
our example environments causes confusion when using a default build
because of the need to escape the space in the build filename.

This change removes the space from the Product Name in the project's
player settings.

* [bug-fix] Increase 3dballhard and GAIL default steps (#3636)

* Updating the NN models (#3632)

* Updating the NN models

* Update gridworld

* [skip ci] Update BallHard

* Update hallway

* [skip ci] Resolve a merging bug

Co-authored-by: Jonathan Harper <jharper+moar@unity3d.com>
Co-authored-by: Chris Elion <chris.elion@unity3d.com>
Co-authored-by: Ervin T <ervin@unity3d.com>

README.md

@@ -29,14 +29,14 @@ developer communities.
 * Unity environment control from Python
 * 15+ sample Unity environments
 * Two deep reinforcement learning algorithms,
-[Proximal Policy Optimization](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Training-PPO.md)
- (PPO) and [Soft Actor-Critic](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Training-SAC.md)
+[Proximal Policy Optimization](docs/Training-PPO.md)
+ (PPO) and [Soft Actor-Critic](docs/Training-SAC.md)
  (SAC)
 * Support for multiple environment configurations and training scenarios
 * Self-play mechanism for training agents in adversarial scenarios
 * Train memory-enhanced agents using deep reinforcement learning
 * Easily definable Curriculum Learning and Generalization scenarios
-* Built-in support for [Imitation Learning](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Training-Imitation-Learning.md) through Behavioral Cloning or Generative Adversarial Imitation Learning
+* Built-in support for [Imitation Learning](docs/Training-Imitation-Learning.md) through Behavioral Cloning or Generative Adversarial Imitation Learning
 * Flexible agent control with On Demand Decision Making
 * Visualizing network outputs within the environment
 * Wrap learning environments as a gym
@@ -46,6 +46,7 @@ developer communities.
 ## Releases & Documentation
 **Our latest, stable release is 0.15.0. Click
 [here](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Readme.md) to
+
 get started with the latest release of ML-Agents.**
 
 The table below lists all our releases, including our `master` branch which is under active