What are some ways to train, test and render an RL autonomous drone in a simulator? I completed the Udacity nanodegree in Deep Learning but found the final project to be extremely difficult since the only visual feedback were from plots via matplotlib (rotor speeds, x/y/z coordinates, x/y/z velocity, etc).  Is there any way to render the learning per episode as done in openai gym?

A quick search for Python game engines only confused me more in terms of which one to use. 

Here is a link to the final project, ie. fly a quadcopter using DDPG algorithm:  https://github.com/udacity/RL-Quadcopter-2

It has a physics_sim.py, task.py, agent.py and a main ipynb notebook.
I tried contacting a few drone simulator makers including DRL (Drone Racing League) and Velocidrone but the latter does not have API for Python and DRL has not responded.  

Therefore, I looked at Unity ML Agents and saw that it's possible to create a simulation environment using free drone assets:

https://unity3d.com/how-to/unity-machine-learning-agents

https://assetstore.unity.com/packages/tools/physics/free-pack-117641

And they have a Python API:

https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md