Simulation environments for RL
As mentioned earlier, trial and error is an important component of any RL algorithm. Therefore, it makes sense to train our RL agent firstly in a simulated environment.
Today there exists a large number of platforms that can be used for the creation of an environment. Some popular ones are:
- OpenAI Gym: This 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 2D/3D and VR/AR games. You can learn more about it here: https://github.com/Unity-Technologies/ml-agents.
- Gazebo: In Gazebo, we can...
