Deep Reinforcement Learning for Drones in 3D realistic environments

A complete code to get you started with implementing Deep Reinforcement Learning in a realistically looking environment using Unreal Gaming Engine and Python.

Overview:

Last week, I made a GitHub repository public that contains a stand-alone detailed python code implementing deep reinforcement learning on a drone in a 3D simulated environment using Unreal Gaming Engine. I decided to cover a detailed documentation in this article. The 3D environments are made on Epic Unreal Gaming engine, and Python is used to interface with the environments and carry out Deep reinforcement learning using TensorFlow.

Drone navigating in a 3D indoor environment.[4]

At the end of this article, you will have a working platform on your machine capable of implementing Deep Reinforcement Learning on a realistically looking environment for a Drone. You will be able to

• Design your custom environments
• Interface it with your Python code
• Use/modify existing Python code for DRL

For this article, the underlying objective will be drone autonomous navigation. There are no start or end positions, rather the drone has to navigate as long as it can without colliding into obstacles. The code can be modified to any user-defined objective.

The complete simulation consists of three major parts

• 3D Simulation Platform — To create and run simulated environments
• Interface Platform — To simulate drone physics and interface between Unreal and Python
• DRL python code Platform — Contains the DRL code based on TensorFlow

There are multiple options to select each of these three platforms. But for this article, we will select the following

• 3D simulation Platform — Unreal Engine [1]
• Interface Platform — AirSim [2]
• DRL python code Platform — DRLwithTL GitHub repository [3]