Simulated environments

Since RL involves trial and error, it makes sense to train our RL agent first in a simulated environment. While a large number of applications exist that can be used for the creation of an environment, some popular ones include the following:

• OpenAI gym: It contains a collection of environments that we can use to train our RL agents. In this chapter, we'll be using the OpenAI gym interface.
• Unity ML-Agents SDK: It allows developers to transform games and simulations created using the Unity editor into environments where intelligent agents can be trained using DRL, evolutionary strategies, or other machine learning methods through a simple-to-use Python API. It works with TensorFlow and provides the ability to train intelligent agents for two-dimensional/three-dimensional and VR/AR games. You can learn more about it here: https://github.com/Unity-Technologies/ml-agents. 
• Gazebo: In Gazebo, we can build three-dimensional worlds with physics-based simulation. Gazebo along...